diff --git a/doc/source/whatsnew/v2.2.1.rst b/doc/source/whatsnew/v2.2.1.rst
index 69e14a9028dd3..cea2204ca8355 100644
--- a/doc/source/whatsnew/v2.2.1.rst
+++ b/doc/source/whatsnew/v2.2.1.rst
@@ -29,7 +29,7 @@ Fixed regressions
 - Fixed regression in :meth:`ExtensionArray.to_numpy` raising for non-numeric masked dtypes (:issue:`56991`)
 - Fixed regression in :meth:`Index.join` raising ``TypeError`` when joining an empty index to a non-empty index containing mixed dtype values (:issue:`57048`)
 - Fixed regression in :meth:`Series.pct_change` raising a ``ValueError`` for an empty :class:`Series` (:issue:`57056`)
-- Fixed regression in :meth:`Series.to_numpy` when dtype is given as float and the data contains NaNs (:issue:`57121`)
+- Fixed regression in :meth:`Series.to_numpy` when dtype is given as float and the data contains NaNs (:issue:`57121`, :issue:`57093`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_221.bug_fixes:
diff --git a/pandas/core/arrays/_utils.py b/pandas/core/arrays/_utils.py
index 88091a88a4e12..88653052ae4e9 100644
--- a/pandas/core/arrays/_utils.py
+++ b/pandas/core/arrays/_utils.py
@@ -46,7 +46,16 @@ def to_numpy_dtype_inference(
         dtype_given = True
 
     if na_value is lib.no_default:
-        na_value = arr.dtype.na_value
+        if dtype is None:
+            na_value = arr.dtype.na_value
+        elif dtype.kind == "f":
+            na_value = np.nan
+        elif dtype.kind == "M":
+            na_value = np.datetime64("nat")
+        elif dtype.kind == "m":
+            na_value = np.timedelta64("nat")
+        else:
+            na_value = arr.dtype.na_value
 
     if not dtype_given and hasna:
         try:
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index 9f8985788cb95..a7ca106f2388d 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -3480,3 +3480,12 @@ def test_to_numpy_timestamp_to_int():
     result = ser.to_numpy(dtype=np.int64)
     expected = np.array([1577853000000000000])
     tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["float64", "datetime64[ns]", "timedelta64[ns]"])
+def test_astype_int_with_null_to_numpy_dtype(dtype):
+    # GH 57093
+    ser = pd.Series([1, None], dtype="int64[pyarrow]")
+    result = ser.astype(dtype)
+    expected = pd.Series([1, None], dtype=dtype)
+    tm.assert_series_equal(result, expected)