diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index ab431b0faee16..7b4457fe477ca 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -581,58 +581,6 @@ def is_dtype_equal(source, target) -> bool:
         return False
 
 
-def is_any_int_dtype(arr_or_dtype) -> bool:
-    """
-    Check whether the provided array or dtype is of an integer dtype.
-
-    In this function, timedelta64 instances are also considered "any-integer"
-    type objects and will return True.
-
-    This function is internal and should not be exposed in the public API.
-
-    The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered
-    as integer by this function.
-
-    Parameters
-    ----------
-    arr_or_dtype : array-like or dtype
-        The array or dtype to check.
-
-    Returns
-    -------
-    boolean
-        Whether or not the array or dtype is of an integer dtype.
-
-    Examples
-    --------
-    >>> is_any_int_dtype(str)
-    False
-    >>> is_any_int_dtype(int)
-    True
-    >>> is_any_int_dtype(float)
-    False
-    >>> is_any_int_dtype(np.uint64)
-    True
-    >>> is_any_int_dtype(np.datetime64)
-    False
-    >>> is_any_int_dtype(np.timedelta64)
-    True
-    >>> is_any_int_dtype(np.array(['a', 'b']))
-    False
-    >>> is_any_int_dtype(pd.Series([1, 2]))
-    True
-    >>> is_any_int_dtype(np.array([], dtype=np.timedelta64))
-    True
-    >>> is_any_int_dtype(pd.Index([1, 2.]))  # float
-    False
-    """
-    return _is_dtype_type(
-        arr_or_dtype, classes(np.integer, np.timedelta64)
-    ) or _is_dtype(
-        arr_or_dtype, lambda typ: isinstance(typ, ExtensionDtype) and typ.kind in "iu"
-    )
-
-
 def is_integer_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of an integer dtype.
@@ -936,10 +884,7 @@ def is_datetime64_ns_dtype(arr_or_dtype) -> bool:
     try:
         tipo = get_dtype(arr_or_dtype)
     except TypeError:
-        if is_datetime64tz_dtype(arr_or_dtype):
-            tipo = get_dtype(arr_or_dtype.dtype)
-        else:
-            return False
+        return False
     return tipo == DT64NS_DTYPE or (
         isinstance(tipo, DatetimeTZDtype) and tipo.unit == "ns"
     )
@@ -1718,7 +1663,6 @@ def is_all_strings(value: ArrayLike) -> bool:
     "is_1d_only_ea_dtype",
     "is_1d_only_ea_obj",
     "is_all_strings",
-    "is_any_int_dtype",
     "is_any_real_numeric_dtype",
     "is_array_like",
     "is_bool",
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index 99f2a7d820dcb..12975499a8d5c 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -39,7 +39,6 @@
 )
 from pandas.errors import PerformanceWarning
 
-from pandas.core.dtypes.common import is_any_int_dtype
 from pandas.core.dtypes.dtypes import CategoricalDtypeType
 
 import pandas as pd
@@ -1584,15 +1583,6 @@ def test_is_integer_dtype(data):
         assert not is_integer_dtype(data)
 
 
-def test_is_any_integer_dtype(data):
-    # GH 50667
-    pa_type = data.dtype.pyarrow_dtype
-    if pa.types.is_integer(pa_type):
-        assert is_any_int_dtype(data)
-    else:
-        assert not is_any_int_dtype(data)
-
-
 def test_is_signed_integer_dtype(data):
     pa_type = data.dtype.pyarrow_dtype
     if pa.types.is_signed_integer(pa_type):