diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index 099e5bc48353a..e4fc0d0471727 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -907,6 +907,7 @@ Other
 - Bug in printing a :class:`Series` with a :class:`DataFrame` stored in :attr:`Series.attrs` raised a ``ValueError`` (:issue:`60568`)
 - Fixed bug where the :class:`DataFrame` constructor misclassified array-like objects with a ``.name`` attribute as :class:`Series` or :class:`Index` (:issue:`61443`)
 - Fixed regression in :meth:`DataFrame.from_records` not initializing subclasses properly (:issue:`57008`)
+- Bug in input validation when coercing object-dtype arrays containing ambiguous datetime strings to ``datetime64`` that could result in silently inconsistent parsing. (:issue:`61353``)
 
 .. ***DO NOT USE THIS SECTION***
 
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 6aa5062b8ed86..a345faa6ec68b 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -2177,6 +2177,40 @@ def maybe_coerce_values(values: ArrayLike) -> ArrayLike:
     if isinstance(values, np.ndarray):
         values = ensure_wrapped_if_datetimelike(values)
 
+        _date_like_re = re.compile(r"\d{1,4}[/\-]\d{1,2}[/\-]\d{1,4}")
+        if (
+            values.dtype == object
+            and values.ndim == 1
+            and len(values) > 0
+            and all(isinstance(x, str) and _date_like_re.match(x) for x in values)
+        ):
+            print("[DEBUG] matched ambiguous datetime regex:", values)
+            from pandas.core.tools.datetimes import (
+                _guess_datetime_format_for_array,
+                to_datetime,
+            )
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", UserWarning)
+                fmt = _guess_datetime_format_for_array(values)
+
+            if fmt is None:
+                raise ValueError(
+                    "Ambiguous datetime string format detected. "
+                    "Specify a format via `pd.to_datetime(..., format=...)` "
+                    "or use `dayfirst=True`."
+                )
+
+            try:
+                # Validate consistent parsing
+                to_datetime(values, format=fmt, dayfirst=False)
+            except ValueError:
+                raise ValueError(
+                    "Inconsistent or ambiguous datetime strings detected. "
+                    "Specify `format=...` "
+                    "or use `dayfirst=True` to ensure correct parsing."
+                ) from None
+
         if issubclass(values.dtype.type, str):
             values = np.array(values, dtype=object)
 
diff --git a/pandas/tests/frame/indexing/test_setitem.py b/pandas/tests/frame/indexing/test_setitem.py
index 20dd7b0c4d3e7..2922e8eedad8c 100644
--- a/pandas/tests/frame/indexing/test_setitem.py
+++ b/pandas/tests/frame/indexing/test_setitem.py
@@ -816,6 +816,18 @@ def test_setitem_index_object_dtype_not_inferring(self):
         )
         tm.assert_frame_equal(df, expected)
 
+    def test_setitem_with_ambiguous_datetime_strings_raises(self):
+        df = DataFrame({"a": date_range("2020-01-01", periods=2)})
+        with pytest.raises(
+            ValueError,
+            match=(
+                "(?i)ambiguous datetime string format|"
+                "inconsistent or ambiguous datetime strings"
+            ),
+        ):
+            ambiguous_dates = Series(["12/01/2020", "13/01/2020"], dtype=object)
+            df.loc[:, "a"] = ambiguous_dates
+
 
 class TestSetitemTZAwareValues:
     @pytest.fixture
@@ -1399,3 +1411,15 @@ def test_setitem_partial_row_multiple_columns():
         }
     )
     tm.assert_frame_equal(df, expected)
+
+
+def test_constructor_with_ambiguous_datetime_strings_raises():
+    with pytest.raises(
+        ValueError,
+        match=(
+            "(?i)ambiguous datetime string format|"
+            "inconsistent or ambiguous datetime strings"
+        ),
+    ):
+        df = DataFrame({"a": Series(["12/01/2020", "13/01/2020"], dtype="object")})
+        df.astype({"a": "datetime64[ns]"})