diff --git a/pandas/_libs/parsers.pyx b/pandas/_libs/parsers.pyx
index 25028b06f7bad..4d87dcefa856d 100644
--- a/pandas/_libs/parsers.pyx
+++ b/pandas/_libs/parsers.pyx
@@ -1090,7 +1090,20 @@ cdef class TextReader:
 
         # we had a fallback parse on the dtype, so now try to cast
         # only allow safe casts, eg. with a nan you cannot safely cast to int
+        #floating casts are handled in this section
         if col_res is not None and col_dtype is not None:
+            if is_float_dtype(col_dtype) and col_res.dtype == np.bool_ :
+                mask = col_res.view(np.uint8) == na_values[np.uint8]
+                col_res = col_res.astype(col_dtype)
+                np.putmask(col_res, mask, np.nan)
+                return col_res, na_count
+            if is_integer_dtype(col_dtype) and col_res.dtype == np.bool_ :
+                if na_count > 0:
+                    raise ValueError(
+                        f"cannot safely convert passed user dtype of "
+                        f"{col_dtype} for {np.bool_} dtyped data in "
+                        f"column {i} as it is applicable only in the case of float values and not NA values")
+                pass
             try:
                 col_res = col_res.astype(col_dtype, casting='safe')
             except TypeError:
diff --git a/pandas/tests/io/parser/test_na_values.py b/pandas/tests/io/parser/test_na_values.py
index 2880bf8690b46..b02ea5d9f4396 100644
--- a/pandas/tests/io/parser/test_na_values.py
+++ b/pandas/tests/io/parser/test_na_values.py
@@ -590,3 +590,22 @@ def test_nan_multi_index(all_parsers):
     )
 
     tm.assert_frame_equal(result, expected)
+
+def test_bool_and_nan_to_int(all_parsers):
+    p = all_parsers
+    test_data_val = """0 NaN True False"""
+    with pytest.raises(ValueError, match = "convert"):
+        print(p.read_csv(StringIO(test_data_val), dtype = "int"))
+
+def test_bool_and_nan_to_float(all_parsers):
+    p = all_parsers
+    test_data_val = """0 NaN True False"""
+    result = p.read_csv(StringIO(test_data_val), dtype = "float")
+    expected = DataFrame.from_dict({"0": [np.nan, 1.0, 0.0]})
+    tm.assert_frame_equal(result, expected)
+
+def test_bool_and_nan_to_bool(all_parsers):
+    p = all_parsers
+    test_data_val = """0 NaN True False """
+    with pytest.raises(ValueError, match = "NA values"):
+        p.read_csv(StringIO(test_data_val), dtype = "bool")