diff --git a/doc/source/whatsnew/v2.1.0.rst b/doc/source/whatsnew/v2.1.0.rst
index a037e50593737..fd19c84f8ab23 100644
--- a/doc/source/whatsnew/v2.1.0.rst
+++ b/doc/source/whatsnew/v2.1.0.rst
@@ -316,7 +316,9 @@ Sparse
 
 ExtensionArray
 ^^^^^^^^^^^^^^
+- Bug where the ``__from_arrow__`` method of masked ExtensionDtypes(e.g. :class:`Float64Dtype`, :class:`BooleanDtype`) would not accept pyarrow arrays of type ``pyarrow.null()`` (:issue:`52223`)
 - Bug in :meth:`Series.rank` returning wrong order for small values with ``Float64`` dtype (:issue:`52471`)
+-
 
 Styler
 ^^^^^^
diff --git a/pandas/core/arrays/arrow/_arrow_utils.py b/pandas/core/arrays/arrow/_arrow_utils.py
index 6e6ef6a2c20a8..2a053fac2985c 100644
--- a/pandas/core/arrays/arrow/_arrow_utils.py
+++ b/pandas/core/arrays/arrow/_arrow_utils.py
@@ -42,6 +42,11 @@ def pyarrow_array_to_numpy_and_mask(
     """
     dtype = np.dtype(dtype)
 
+    if pyarrow.types.is_null(arr.type):
+        # No initialization of data is needed since everything is null
+        data = np.empty(len(arr), dtype=dtype)
+        mask = np.zeros(len(arr), dtype=bool)
+        return data, mask
     buflist = arr.buffers()
     # Since Arrow buffers might contain padding and the data might be offset,
     # the buffer gets sliced here before handing it to numpy.
diff --git a/pandas/core/arrays/boolean.py b/pandas/core/arrays/boolean.py
index 54bd4220bc060..f6bc8a87a4c60 100644
--- a/pandas/core/arrays/boolean.py
+++ b/pandas/core/arrays/boolean.py
@@ -108,14 +108,22 @@ def __from_arrow__(
         """
         import pyarrow
 
-        if array.type != pyarrow.bool_():
+        if array.type != pyarrow.bool_() and not pyarrow.types.is_null(array.type):
             raise TypeError(f"Expected array of boolean type, got {array.type} instead")
 
         if isinstance(array, pyarrow.Array):
             chunks = [array]
+            length = len(array)
         else:
             # pyarrow.ChunkedArray
             chunks = array.chunks
+            length = array.length()
+
+        if pyarrow.types.is_null(array.type):
+            mask = np.ones(length, dtype=bool)
+            # No need to init data, since all null
+            data = np.empty(length, dtype=bool)
+            return BooleanArray(data, mask)
 
         results = []
         for arr in chunks:
diff --git a/pandas/core/arrays/numeric.py b/pandas/core/arrays/numeric.py
index 8d629b88edd26..344946ad68d32 100644
--- a/pandas/core/arrays/numeric.py
+++ b/pandas/core/arrays/numeric.py
@@ -76,7 +76,9 @@ def __from_arrow__(
         array_class = self.construct_array_type()
 
         pyarrow_type = pyarrow.from_numpy_dtype(self.type)
-        if not array.type.equals(pyarrow_type):
+        if not array.type.equals(pyarrow_type) and not pyarrow.types.is_null(
+            array.type
+        ):
             # test_from_arrow_type_error raise for string, but allow
             #  through itemsize conversion GH#31896
             rt_dtype = pandas_dtype(array.type.to_pandas_dtype())
diff --git a/pandas/tests/arrays/masked/test_arrow_compat.py b/pandas/tests/arrays/masked/test_arrow_compat.py
index 6b0081321ef22..fc2094bd9f4a8 100644
--- a/pandas/tests/arrays/masked/test_arrow_compat.py
+++ b/pandas/tests/arrays/masked/test_arrow_compat.py
@@ -184,6 +184,15 @@ def test_pyarrow_array_to_numpy_and_mask(np_dtype_to_arrays):
     tm.assert_numpy_array_equal(mask, mask_expected_empty)
 
 
+@pytest.mark.parametrize(
+    "arr", [pa.nulls(10), pa.chunked_array([pa.nulls(4), pa.nulls(6)])]
+)
+def test_from_arrow_null(data, arr):
+    res = data.dtype.__from_arrow__(arr)
+    assert res.isna().all()
+    assert len(res) == 10
+
+
 def test_from_arrow_type_error(data):
     # ensure that __from_arrow__ returns a TypeError when getting a wrong
     # array type