diff --git a/doc/source/whatsnew/v2.1.0.rst b/doc/source/whatsnew/v2.1.0.rst
index 52fc8512c9db3..e659bf7adbf1a 100644
--- a/doc/source/whatsnew/v2.1.0.rst
+++ b/doc/source/whatsnew/v2.1.0.rst
@@ -351,6 +351,7 @@ Conversion
 - Bug in :meth:`ArrowDtype.numpy_dtype` returning nanosecond units for non-nanosecond ``pyarrow.timestamp`` and ``pyarrow.duration`` types (:issue:`51800`)
 - Bug in :meth:`DataFrame.__repr__` incorrectly raising a ``TypeError`` when the dtype of a column is ``np.record`` (:issue:`48526`)
 - Bug in :meth:`DataFrame.info` raising  ``ValueError`` when ``use_numba`` is set (:issue:`51922`)
+- Bug in :meth:`DataFrame.insert` raising ``TypeError`` if ``loc`` is ``np.int64`` (:issue:`53193`)
 -
 
 Strings
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 485cc9db5ffe7..459b162e2a6c9 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -4800,9 +4800,10 @@ def insert(
         if not allow_duplicates and column in self.columns:
             # Should this be a different kind of error??
             raise ValueError(f"cannot insert {column}, already exists")
-        if not isinstance(loc, int):
+        if not is_integer(loc):
             raise TypeError("loc must be int")
-
+        # convert non stdlib ints to satisfy typing checks
+        loc = int(loc)
         if isinstance(value, DataFrame) and len(value.columns) > 1:
             raise ValueError(
                 f"Expected a one-dimensional object, got a DataFrame with "
diff --git a/pandas/tests/frame/indexing/test_insert.py b/pandas/tests/frame/indexing/test_insert.py
index 666a6ec3710a6..13e43abe0dd7f 100644
--- a/pandas/tests/frame/indexing/test_insert.py
+++ b/pandas/tests/frame/indexing/test_insert.py
@@ -110,3 +110,9 @@ def test_insert_frame(self):
         )
         with pytest.raises(ValueError, match=msg):
             df.insert(1, "newcol", df)
+
+    def test_insert_int64_loc(self):
+        # GH#53193
+        df = DataFrame({"a": [1, 2]})
+        df.insert(np.int64(0), "b", 0)
+        tm.assert_frame_equal(df, DataFrame({"b": [0, 0], "a": [1, 2]}))