diff --git a/doc/source/getting_started/comparison/comparison_with_r.rst b/doc/source/getting_started/comparison/comparison_with_r.rst
index a6cfcd4614984..25ba237e8caf3 100644
--- a/doc/source/getting_started/comparison/comparison_with_r.rst
+++ b/doc/source/getting_started/comparison/comparison_with_r.rst
@@ -438,7 +438,7 @@ In Python, the :meth:`~pandas.melt` method is the R equivalent:
    )
 
    pd.melt(cheese, id_vars=["first", "last"])
-   cheese.set_index(["first", "last"]).stack(future_stack=True)  # alternative way
+   cheese.set_index(["first", "last"]).stack()  # alternative way
 
 For more details and examples see :ref:`the reshaping documentation
 <reshaping.melt>`.
diff --git a/doc/source/user_guide/10min.rst b/doc/source/user_guide/10min.rst
index 7643828ca5a63..0c3307cdd7473 100644
--- a/doc/source/user_guide/10min.rst
+++ b/doc/source/user_guide/10min.rst
@@ -563,7 +563,7 @@ columns:
 
 .. ipython:: python
 
-   stacked = df2.stack(future_stack=True)
+   stacked = df2.stack()
    stacked
 
 With a "stacked" DataFrame or Series (having a :class:`MultiIndex` as the
diff --git a/doc/source/user_guide/cookbook.rst b/doc/source/user_guide/cookbook.rst
index b1a6aa8753be1..3dfc6534f2b64 100644
--- a/doc/source/user_guide/cookbook.rst
+++ b/doc/source/user_guide/cookbook.rst
@@ -311,7 +311,7 @@ The :ref:`multindexing <advanced.hierarchical>` docs.
    df.columns = pd.MultiIndex.from_tuples([tuple(c.split("_")) for c in df.columns])
    df
    # Now stack & Reset
-   df = df.stack(0, future_stack=True).reset_index(1)
+   df = df.stack(0).reset_index(1)
    df
    # And fix the labels (Notice the label 'level_1' got added automatically)
    df.columns = ["Sample", "All_X", "All_Y"]
@@ -688,7 +688,7 @@ The :ref:`Pivot <reshaping.pivot>` docs.
        aggfunc="sum",
        margins=True,
    )
-   table.stack("City", future_stack=True)
+   table.stack("City")
 
 `Frequency table like plyr in R
 <https://stackoverflow.com/questions/15589354/frequency-tables-in-pandas-like-plyr-in-r>`__
diff --git a/doc/source/user_guide/groupby.rst b/doc/source/user_guide/groupby.rst
index 19c2abf10651d..7f957a8b16787 100644
--- a/doc/source/user_guide/groupby.rst
+++ b/doc/source/user_guide/groupby.rst
@@ -1721,4 +1721,4 @@ column index name will be used as the name of the inserted column:
 
    result
 
-   result.stack(future_stack=True)
+   result.stack()
diff --git a/doc/source/user_guide/reshaping.rst b/doc/source/user_guide/reshaping.rst
index a35cfb396f1f6..3347f3a2534f4 100644
--- a/doc/source/user_guide/reshaping.rst
+++ b/doc/source/user_guide/reshaping.rst
@@ -163,7 +163,7 @@ as having a multi-level index:
 
 .. ipython:: python
 
-    table.stack(future_stack=True)
+    table.stack()
 
 .. _reshaping.stacking:
 
@@ -209,7 +209,7 @@ stacked level becomes the new lowest level in a :class:`MultiIndex` on the colum
 
 .. ipython:: python
 
-   stacked = df2.stack(future_stack=True)
+   stacked = df2.stack()
    stacked
 
 With a "stacked" :class:`DataFrame` or :class:`Series` (having a :class:`MultiIndex` as the
@@ -245,7 +245,7 @@ will result in a **sorted** copy of the original :class:`DataFrame` or :class:`S
    index = pd.MultiIndex.from_product([[2, 1], ["a", "b"]])
    df = pd.DataFrame(np.random.randn(4), index=index, columns=["A"])
    df
-   all(df.unstack().stack(future_stack=True) == df.sort_index())
+   all(df.unstack().stack() == df.sort_index())
 
 .. _reshaping.stack_multiple:
 
@@ -270,16 +270,16 @@ processed individually.
     df = pd.DataFrame(np.random.randn(4, 4), columns=columns)
     df
 
-    df.stack(level=["animal", "hair_length"], future_stack=True)
+    df.stack(level=["animal", "hair_length"])
 
 The list of levels can contain either level names or level numbers but
 not a mixture of the two.
 
 .. ipython:: python
 
-    # df.stack(level=['animal', 'hair_length'], future_stack=True)
+    # df.stack(level=['animal', 'hair_length'])
     # from above is equivalent to:
-    df.stack(level=[1, 2], future_stack=True)
+    df.stack(level=[1, 2])
 
 Missing data
 ~~~~~~~~~~~~
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index e07ee3cbe2867..f8edbfffd5af0 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -108,6 +108,7 @@ Removal of prior version deprecations/changes
 - All arguments except the first ``path``-like argument in IO writers are now keyword only (:issue:`54229`)
 - Changed the default value of ``observed`` in :meth:`DataFrame.groupby` and :meth:`Series.groupby` to ``True`` (:issue:`51811`)
 - Enforced silent-downcasting deprecation for :ref:`all relevant methods <whatsnew_220.silent_downcasting>` (:issue:`54710`)
+- In :meth:`DataFrame.stack`, the default value of ``future_stack`` is now ``True``; specifying ``False`` will raise a ``FutureWarning`` (:issue:`55448`)
 - Removed ``DataFrame.applymap``, ``Styler.applymap`` and ``Styler.applymap_index`` (:issue:`52364`)
 - Removed ``DataFrame.bool`` and ``Series.bool`` (:issue:`51756`)
 - Removed ``DataFrame.first`` and ``DataFrame.last`` (:issue:`53710`)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 927518b766cc2..923385d090078 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -9191,7 +9191,7 @@ def stack(
         level: IndexLabel = -1,
         dropna: bool | lib.NoDefault = lib.no_default,
         sort: bool | lib.NoDefault = lib.no_default,
-        future_stack: bool = False,
+        future_stack: bool = True,
     ):
         """
         Stack the prescribed level(s) from columns to index.
@@ -9264,7 +9264,7 @@ def stack(
              weight height
         cat       0      1
         dog       2      3
-        >>> df_single_level_cols.stack(future_stack=True)
+        >>> df_single_level_cols.stack()
         cat  weight    0
              height    1
         dog  weight    2
@@ -9287,7 +9287,7 @@ def stack(
                  kg    pounds
         cat       1        2
         dog       2        4
-        >>> df_multi_level_cols1.stack(future_stack=True)
+        >>> df_multi_level_cols1.stack()
                     weight
         cat kg           1
             pounds       2
@@ -9311,7 +9311,7 @@ def stack(
                 kg      m
         cat    1.0    2.0
         dog    3.0    4.0
-        >>> df_multi_level_cols2.stack(future_stack=True)
+        >>> df_multi_level_cols2.stack()
                 weight  height
         cat kg     1.0     NaN
             m      NaN     2.0
@@ -9322,13 +9322,13 @@ def stack(
 
         The first parameter controls which level or levels are stacked:
 
-        >>> df_multi_level_cols2.stack(0, future_stack=True)
+        >>> df_multi_level_cols2.stack(0)
                      kg    m
         cat weight  1.0  NaN
             height  NaN  2.0
         dog weight  3.0  NaN
             height  NaN  4.0
-        >>> df_multi_level_cols2.stack([0, 1], future_stack=True)
+        >>> df_multi_level_cols2.stack([0, 1])
         cat  weight  kg    1.0
              height  m     2.0
         dog  weight  kg    3.0
@@ -9341,19 +9341,14 @@ def stack(
                 stack_multiple,
             )
 
-            if (
-                dropna is not lib.no_default
-                or sort is not lib.no_default
-                or self.columns.nlevels > 1
-            ):
-                warnings.warn(
-                    "The previous implementation of stack is deprecated and will be "
-                    "removed in a future version of pandas. See the What's New notes "
-                    "for pandas 2.1.0 for details. Specify future_stack=True to adopt "
-                    "the new implementation and silence this warning.",
-                    FutureWarning,
-                    stacklevel=find_stack_level(),
-                )
+            warnings.warn(
+                "The previous implementation of stack is deprecated and will be "
+                "removed in a future version of pandas. See the What's New notes "
+                "for pandas 2.1.0 for details. Do not specify the future_stack "
+                "argument to adopt the new implementation and silence this warning.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
 
             if dropna is lib.no_default:
                 dropna = True
@@ -9369,14 +9364,14 @@ def stack(
 
             if dropna is not lib.no_default:
                 raise ValueError(
-                    "dropna must be unspecified with future_stack=True as the new "
+                    "dropna must be unspecified as the new "
                     "implementation does not introduce rows of NA values. This "
                     "argument will be removed in a future version of pandas."
                 )
 
             if sort is not lib.no_default:
                 raise ValueError(
-                    "Cannot specify sort with future_stack=True, this argument will be "
+                    "Cannot specify sort, this argument will be "
                     "removed in a future version of pandas. Sort the result using "
                     ".sort_index instead."
                 )
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 30559ae91ce41..dfe145783c4a3 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -513,7 +513,7 @@ def _wrap_applied_output(
             res_df = self.obj._constructor_expanddim(values, index=index)
             # if self.observed is False,
             # keep all-NaN rows created while re-indexing
-            res_ser = res_df.stack(future_stack=True)
+            res_ser = res_df.stack()
             res_ser.name = self.obj.name
             return res_ser
         elif isinstance(values[0], (Series, DataFrame)):
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index cbe280b9184c3..e60dcdb10e653 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -1370,7 +1370,7 @@ def size(self):
         # If the result is a non-empty DataFrame we stack to get a Series
         # GH 46826
         if isinstance(result, ABCDataFrame) and not result.empty:
-            result = result.stack(future_stack=True)
+            result = result.stack()
 
         if not len(self.ax):
             from pandas import Series
diff --git a/pandas/core/reshape/pivot.py b/pandas/core/reshape/pivot.py
index bd927472f06d3..f0eb7f44bf34e 100644
--- a/pandas/core/reshape/pivot.py
+++ b/pandas/core/reshape/pivot.py
@@ -420,7 +420,7 @@ def _all_key(key):
 
     if len(cols) > 0:
         row_margin = data[cols + values].groupby(cols, observed=observed).agg(aggfunc)
-        row_margin = row_margin.stack(future_stack=True)
+        row_margin = row_margin.stack()
 
         # GH#26568. Use names instead of indices in case of numeric names
         new_order_indices = [len(cols)] + list(range(len(cols)))
diff --git a/pandas/core/reshape/reshape.py b/pandas/core/reshape/reshape.py
index bb544b588dd35..e8ef393da38d9 100644
--- a/pandas/core/reshape/reshape.py
+++ b/pandas/core/reshape/reshape.py
@@ -518,7 +518,7 @@ def unstack(
         if isinstance(obj.index, MultiIndex):
             return _unstack_frame(obj, level, fill_value=fill_value, sort=sort)
         else:
-            return obj.T.stack(future_stack=True)
+            return obj.T.stack()
     elif not isinstance(obj.index, MultiIndex):
         # GH 36113
         # Give nicer error messages when unstack a Series whose
diff --git a/pandas/tests/extension/json/test_json.py b/pandas/tests/extension/json/test_json.py
index 1e4897368cc7a..5657cb92f72fc 100644
--- a/pandas/tests/extension/json/test_json.py
+++ b/pandas/tests/extension/json/test_json.py
@@ -127,7 +127,7 @@ def test_series_constructor_scalar_with_index(self, data, dtype):
     @pytest.mark.xfail(reason="Different definitions of NA")
     def test_stack(self):
         """
-        The test does .astype(object).stack(future_stack=True). If we happen to have
+        The test does .astype(object).stack(). If we happen to have
         any missing values in `data`, then we'll end up with different
         rows since we consider `{}` NA, but `.astype(object)` doesn't.
         """
diff --git a/pandas/tests/frame/methods/test_reset_index.py b/pandas/tests/frame/methods/test_reset_index.py
index a77e063b5f353..22ce091d4ed62 100644
--- a/pandas/tests/frame/methods/test_reset_index.py
+++ b/pandas/tests/frame/methods/test_reset_index.py
@@ -106,7 +106,7 @@ def test_reset_index_with_intervals(self):
         tm.assert_frame_equal(result2, original)
 
     def test_reset_index(self, float_frame):
-        stacked = float_frame.stack(future_stack=True)[::2]
+        stacked = float_frame.stack()[::2]
         stacked = DataFrame({"foo": stacked, "bar": stacked})
 
         names = ["first", "second"]
@@ -739,7 +739,7 @@ def test_reset_index_rename(float_frame):
 
 def test_reset_index_rename_multiindex(float_frame):
     # GH 6878
-    stacked_df = float_frame.stack(future_stack=True)[::2]
+    stacked_df = float_frame.stack()[::2]
     stacked_df = DataFrame({"foo": stacked_df, "bar": stacked_df})
 
     names = ["first", "second"]
@@ -753,7 +753,7 @@ def test_reset_index_rename_multiindex(float_frame):
 
 def test_errorreset_index_rename(float_frame):
     # GH 6878
-    stacked_df = float_frame.stack(future_stack=True)[::2]
+    stacked_df = float_frame.stack()[::2]
     stacked_df = DataFrame({"first": stacked_df, "second": stacked_df})
 
     with pytest.raises(
diff --git a/pandas/tests/frame/test_stack_unstack.py b/pandas/tests/frame/test_stack_unstack.py
index 15426b117614c..b73eb21d21c08 100644
--- a/pandas/tests/frame/test_stack_unstack.py
+++ b/pandas/tests/frame/test_stack_unstack.py
@@ -28,6 +28,9 @@ def future_stack(request):
 
 
 class TestDataFrameReshape:
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
     def test_stack_unstack(self, float_frame, future_stack):
         df = float_frame.copy()
         df[:] = np.arange(np.prod(df.shape)).reshape(df.shape)
@@ -1157,6 +1160,9 @@ def test_stack_full_multiIndex(self, future_stack):
         expected["B"] = expected["B"].astype(df.dtypes.iloc[0])
         tm.assert_frame_equal(result, expected)
 
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
     @pytest.mark.parametrize("ordered", [False, True])
     def test_stack_preserve_categorical_dtype(self, ordered, future_stack):
         # GH13854
@@ -1201,6 +1207,9 @@ def test_stack_multi_preserve_categorical_dtype(
 
         tm.assert_series_equal(result, expected)
 
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
     def test_stack_preserve_categorical_dtype_values(self, future_stack):
         # GH-23077
         cat = pd.Categorical(["a", "a", "b", "c"])
@@ -1393,6 +1402,7 @@ def test_unstack_timezone_aware_values():
     tm.assert_frame_equal(result, expected)
 
 
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
 def test_stack_timezone_aware_values(future_stack):
     # GH 19420
     ts = date_range(freq="D", start="20180101", end="20180103", tz="America/New_York")
@@ -1719,6 +1729,9 @@ def test_stack(self, multiindex_year_month_day_dataframe_random_data, future_sta
         expected = ymd.unstack(0).stack(0, future_stack=future_stack)
         tm.assert_equal(result, expected)
 
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
     @pytest.mark.parametrize(
         "idx, exp_idx",
         [
@@ -1805,6 +1818,9 @@ def test_stack_mixed_dtype(self, multiindex_dataframe_random_data, future_stack)
         assert result.name is None
         assert stacked["bar"].dtype == np.float64
 
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
     def test_unstack_bug(self, future_stack):
         df = DataFrame(
             {
@@ -1839,6 +1855,9 @@ def test_stack_unstack_preserve_names(
         restacked = unstacked.stack(future_stack=future_stack)
         assert restacked.index.names == frame.index.names
 
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
     @pytest.mark.parametrize("method", ["stack", "unstack"])
     def test_stack_unstack_wrong_level_name(
         self, method, multiindex_dataframe_random_data, future_stack
@@ -2308,6 +2327,9 @@ def test_unstack_preserve_types(
         )
         assert unstacked["F", 1].dtype == np.float64
 
+    @pytest.mark.filterwarnings(
+        "ignore:The previous implementation of stack is deprecated"
+    )
     def test_unstack_group_index_overflow(self, future_stack):
         codes = np.tile(np.arange(500), 2)
         level = np.arange(500)
@@ -2610,6 +2632,7 @@ def test_unstack_mixed_level_names(self):
         tm.assert_frame_equal(result, expected)
 
 
+@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated")
 def test_stack_tuple_columns(future_stack):
     # GH#54948 - test stack when the input has a non-MultiIndex with tuples
     df = DataFrame(
@@ -2643,7 +2666,7 @@ def test_stack_preserves_na(dtype, na_value, test_multiindex):
     else:
         index = Index([na_value], dtype=dtype)
     df = DataFrame({"a": [1]}, index=index)
-    result = df.stack(future_stack=True)
+    result = df.stack()
 
     if test_multiindex:
         expected_index = MultiIndex.from_arrays(
diff --git a/pandas/tests/frame/test_subclass.py b/pandas/tests/frame/test_subclass.py
index 91f5de8e7d7f3..316216e69a587 100644
--- a/pandas/tests/frame/test_subclass.py
+++ b/pandas/tests/frame/test_subclass.py
@@ -208,7 +208,7 @@ def test_subclass_stack(self):
             columns=["X", "Y", "Z"],
         )
 
-        res = df.stack(future_stack=True)
+        res = df.stack()
         exp = tm.SubclassedSeries(
             [1, 2, 3, 4, 5, 6, 7, 8, 9], index=[list("aaabbbccc"), list("XYZXYZXYZ")]
         )
@@ -245,10 +245,10 @@ def test_subclass_stack_multi(self):
             columns=Index(["W", "X"], name="www"),
         )
 
-        res = df.stack(future_stack=True)
+        res = df.stack()
         tm.assert_frame_equal(res, exp)
 
-        res = df.stack("yyy", future_stack=True)
+        res = df.stack("yyy")
         tm.assert_frame_equal(res, exp)
 
         exp = tm.SubclassedDataFrame(
@@ -269,7 +269,7 @@ def test_subclass_stack_multi(self):
             columns=Index(["y", "z"], name="yyy"),
         )
 
-        res = df.stack("www", future_stack=True)
+        res = df.stack("www")
         tm.assert_frame_equal(res, exp)
 
     def test_subclass_stack_multi_mixed(self):
@@ -307,10 +307,10 @@ def test_subclass_stack_multi_mixed(self):
             columns=Index(["W", "X"], name="www"),
         )
 
-        res = df.stack(future_stack=True)
+        res = df.stack()
         tm.assert_frame_equal(res, exp)
 
-        res = df.stack("yyy", future_stack=True)
+        res = df.stack("yyy")
         tm.assert_frame_equal(res, exp)
 
         exp = tm.SubclassedDataFrame(
@@ -331,7 +331,7 @@ def test_subclass_stack_multi_mixed(self):
             columns=Index(["y", "z"], name="yyy"),
         )
 
-        res = df.stack("www", future_stack=True)
+        res = df.stack("www")
         tm.assert_frame_equal(res, exp)
 
     def test_subclass_unstack(self):
diff --git a/pandas/tests/groupby/aggregate/test_cython.py b/pandas/tests/groupby/aggregate/test_cython.py
index b1f8ecc9c8a39..19eef06de9136 100644
--- a/pandas/tests/groupby/aggregate/test_cython.py
+++ b/pandas/tests/groupby/aggregate/test_cython.py
@@ -68,7 +68,7 @@ def test_cythonized_aggers(op_name):
     expd = {}
     for (cat1, cat2), group in grouped:
         expd.setdefault(cat1, {})[cat2] = op(group["C"])
-    exp = DataFrame(expd).T.stack(future_stack=True)
+    exp = DataFrame(expd).T.stack()
     exp.index.names = ["A", "B"]
     exp.name = "C"
 
diff --git a/pandas/tests/groupby/methods/test_describe.py b/pandas/tests/groupby/methods/test_describe.py
index 274e23abd77d9..3b6fbfa0a31fc 100644
--- a/pandas/tests/groupby/methods/test_describe.py
+++ b/pandas/tests/groupby/methods/test_describe.py
@@ -35,7 +35,7 @@ def test_series_describe_single():
     )
     grouped = ts.groupby(lambda x: x.month)
     result = grouped.apply(lambda x: x.describe())
-    expected = grouped.describe().stack(future_stack=True)
+    expected = grouped.describe().stack()
     tm.assert_series_equal(result, expected)
 
 
diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py
index d8014558c52b2..10eca5ea8427f 100644
--- a/pandas/tests/groupby/test_categorical.py
+++ b/pandas/tests/groupby/test_categorical.py
@@ -240,13 +240,9 @@ def f(x):
     # GH 10460
     expc = Categorical.from_codes(np.arange(4).repeat(8), levels, ordered=True)
     exp = CategoricalIndex(expc)
-    tm.assert_index_equal(
-        (desc_result.stack(future_stack=True).index.get_level_values(0)), exp
-    )
+    tm.assert_index_equal((desc_result.stack().index.get_level_values(0)), exp)
     exp = Index(["count", "mean", "std", "min", "25%", "50%", "75%", "max"] * 4)
-    tm.assert_index_equal(
-        (desc_result.stack(future_stack=True).index.get_level_values(1)), exp
-    )
+    tm.assert_index_equal((desc_result.stack().index.get_level_values(1)), exp)
 
 
 def test_level_get_group(observed):
@@ -684,13 +680,9 @@ def test_datetime():
     # GH 10460
     expc = Categorical.from_codes(np.arange(4).repeat(8), levels, ordered=True)
     exp = CategoricalIndex(expc)
-    tm.assert_index_equal(
-        (desc_result.stack(future_stack=True).index.get_level_values(0)), exp
-    )
+    tm.assert_index_equal((desc_result.stack().index.get_level_values(0)), exp)
     exp = Index(["count", "mean", "std", "min", "25%", "50%", "75%", "max"] * 4)
-    tm.assert_index_equal(
-        (desc_result.stack(future_stack=True).index.get_level_values(1)), exp
-    )
+    tm.assert_index_equal((desc_result.stack().index.get_level_values(1)), exp)
 
 
 def test_categorical_index():
@@ -728,10 +720,8 @@ def test_describe_categorical_columns():
     df = DataFrame(np.random.default_rng(2).standard_normal((20, 4)), columns=cats)
     result = df.groupby([1, 2, 3, 4] * 5).describe()
 
-    tm.assert_index_equal(result.stack(future_stack=True).columns, cats)
-    tm.assert_categorical_equal(
-        result.stack(future_stack=True).columns.values, cats.values
-    )
+    tm.assert_index_equal(result.stack().columns, cats)
+    tm.assert_categorical_equal(result.stack().columns.values, cats.values)
 
 
 def test_unstack_categorical():
diff --git a/pandas/tests/indexes/multi/test_indexing.py b/pandas/tests/indexes/multi/test_indexing.py
index f426a3ee42566..12adbeefa680a 100644
--- a/pandas/tests/indexes/multi/test_indexing.py
+++ b/pandas/tests/indexes/multi/test_indexing.py
@@ -43,12 +43,12 @@ def test_slice_locs(self):
             columns=Index(list("ABCD"), dtype=object),
             index=date_range("2000-01-01", periods=50, freq="B"),
         )
-        stacked = df.stack(future_stack=True)
+        stacked = df.stack()
         idx = stacked.index
 
         slob = slice(*idx.slice_locs(df.index[5], df.index[15]))
         sliced = stacked[slob]
-        expected = df[5:16].stack(future_stack=True)
+        expected = df[5:16].stack()
         tm.assert_almost_equal(sliced.values, expected.values)
 
         slob = slice(
@@ -58,7 +58,7 @@ def test_slice_locs(self):
             )
         )
         sliced = stacked[slob]
-        expected = df[6:15].stack(future_stack=True)
+        expected = df[6:15].stack()
         tm.assert_almost_equal(sliced.values, expected.values)
 
     def test_slice_locs_with_type_mismatch(self):
@@ -67,7 +67,7 @@ def test_slice_locs_with_type_mismatch(self):
             columns=Index(list("ABCD"), dtype=object),
             index=date_range("2000-01-01", periods=10, freq="B"),
         )
-        stacked = df.stack(future_stack=True)
+        stacked = df.stack()
         idx = stacked.index
         with pytest.raises(TypeError, match="^Level type mismatch"):
             idx.slice_locs((1, 3))
@@ -78,7 +78,7 @@ def test_slice_locs_with_type_mismatch(self):
             index=Index([f"i-{i}" for i in range(5)], name="a"),
             columns=Index([f"i-{i}" for i in range(5)], name="a"),
         )
-        stacked = df.stack(future_stack=True)
+        stacked = df.stack()
         idx = stacked.index
         with pytest.raises(TypeError, match="^Level type mismatch"):
             idx.slice_locs(timedelta(seconds=30))
diff --git a/pandas/tests/indexes/multi/test_integrity.py b/pandas/tests/indexes/multi/test_integrity.py
index 9fabd8622a108..f6d960bd41925 100644
--- a/pandas/tests/indexes/multi/test_integrity.py
+++ b/pandas/tests/indexes/multi/test_integrity.py
@@ -247,7 +247,7 @@ def test_rangeindex_fallback_coercion_bug():
     df1 = pd.DataFrame(np.arange(100).reshape((10, 10)))
     df2 = pd.DataFrame(np.arange(100).reshape((10, 10)))
     df = pd.concat(
-        {"df1": df1.stack(future_stack=True), "df2": df2.stack(future_stack=True)},
+        {"df1": df1.stack(), "df2": df2.stack()},
         axis=1,
     )
     df.index.names = ["fizz", "buzz"]
diff --git a/pandas/tests/io/json/test_pandas.py b/pandas/tests/io/json/test_pandas.py
index b9d97e7b75436..28b5fa57d4109 100644
--- a/pandas/tests/io/json/test_pandas.py
+++ b/pandas/tests/io/json/test_pandas.py
@@ -1876,7 +1876,7 @@ def test_json_multiindex(self):
             '{"(0, \'x\')":1,"(0, \'y\')":"a","(1, \'x\')":2,'
             '"(1, \'y\')":"b","(2, \'x\')":3,"(2, \'y\')":"c"}'
         )
-        series = dataframe.stack(future_stack=True)
+        series = dataframe.stack()
         result = series.to_json(orient="index")
         assert result == expected
 
@@ -1915,7 +1915,7 @@ def test_to_json_multiindex_escape(self):
             True,
             index=date_range("2017-01-20", "2017-01-23"),
             columns=["foo", "bar"],
-        ).stack(future_stack=True)
+        ).stack()
         result = df.to_json()
         expected = (
             "{\"(Timestamp('2017-01-20 00:00:00'), 'foo')\":true,"
diff --git a/pandas/tests/io/pytables/test_append.py b/pandas/tests/io/pytables/test_append.py
index 706610316ef43..aff08e5ad3882 100644
--- a/pandas/tests/io/pytables/test_append.py
+++ b/pandas/tests/io/pytables/test_append.py
@@ -141,7 +141,7 @@ def test_append_series(setup_path):
         mi["C"] = "foo"
         mi.loc[3:5, "C"] = "bar"
         mi.set_index(["C", "B"], inplace=True)
-        s = mi.stack(future_stack=True)
+        s = mi.stack()
         s.index = s.index.droplevel(2)
         store.append("mi", s)
         tm.assert_series_equal(store["mi"], s, check_index_type=True)
diff --git a/pandas/tests/series/methods/test_reset_index.py b/pandas/tests/series/methods/test_reset_index.py
index 48e2608a1032a..32b6cbd5acce3 100644
--- a/pandas/tests/series/methods/test_reset_index.py
+++ b/pandas/tests/series/methods/test_reset_index.py
@@ -39,7 +39,7 @@ def test_reset_index(self):
             columns=Index(list("ABCD"), dtype=object),
             index=Index([f"i-{i}" for i in range(30)], dtype=object),
         )[:5]
-        ser = df.stack(future_stack=True)
+        ser = df.stack()
         ser.index.names = ["hash", "category"]
 
         ser.name = "value"
diff --git a/pandas/tests/series/methods/test_unstack.py b/pandas/tests/series/methods/test_unstack.py
index ad11827117209..f9e6dc644e908 100644
--- a/pandas/tests/series/methods/test_unstack.py
+++ b/pandas/tests/series/methods/test_unstack.py
@@ -140,7 +140,7 @@ def test_unstack_multi_index_categorical_values():
         columns=Index(list("ABCD"), dtype=object),
         index=date_range("2000-01-01", periods=10, freq="B"),
     )
-    mi = df.stack(future_stack=True).index.rename(["major", "minor"])
+    mi = df.stack().index.rename(["major", "minor"])
     ser = Series(["foo"] * len(mi), index=mi, name="category", dtype="category")
 
     result = ser.unstack()