diff --git a/python/pyarrow/table.pxi b/python/pyarrow/table.pxi
index 17e787149a7..27e0144d758 100644
--- a/python/pyarrow/table.pxi
+++ b/python/pyarrow/table.pxi
@@ -2886,20 +2886,23 @@ cdef class Table(_PandasConvertible):
         """
         Select rows from the table.
 
-        See :func:`pyarrow.compute.filter` for full usage.
+        The Table can be filtered based on a mask, which will be passed to
+        :func:`pyarrow.compute.filter` to perform the filtering, or it can
+        be filtered through a boolean :class:`.Expression`
 
         Parameters
         ----------
-        mask : Array or array-like
-            The boolean mask to filter the table with.
+        mask : Array or array-like or .Expression
+            The boolean mask or the :class:`.Expression` to filter the table with.
         null_selection_behavior
-            How nulls in the mask should be handled.
+            How nulls in the mask should be handled, does nothing if
+            an :class:`.Expression` is used.
 
         Returns
         -------
         filtered : Table
             A table of the same schema, with only the rows selected
-            by the boolean mask.
+            by applied filtering
 
         Examples
         --------
@@ -2932,7 +2935,11 @@ cdef class Table(_PandasConvertible):
         n_legs: [[2,4,null]]
         animals: [["Flamingo","Horse",null]]
         """
-        return _pc().filter(self, mask, null_selection_behavior)
+        if isinstance(mask, _pc().Expression):
+            return _pc()._exec_plan._filter_table(self, mask,
+                                                  output_type=Table)
+        else:
+            return _pc().filter(self, mask, null_selection_behavior)
 
     def take(self, object indices):
         """
diff --git a/python/pyarrow/tests/test_dataset.py b/python/pyarrow/tests/test_dataset.py
index 0520bdedc30..3bc905f2f97 100644
--- a/python/pyarrow/tests/test_dataset.py
+++ b/python/pyarrow/tests/test_dataset.py
@@ -4620,3 +4620,19 @@ def test_dataset_join_collisions(tempdir):
         [10, 20, None, 99],
         ["A", "B", None, "Z"],
     ], names=["colA", "colB", "colVals", "colB_r", "colVals_r"])
+
+
+@pytest.mark.dataset
+def test_dataset_filter(tempdir):
+    t1 = pa.table({
+        "colA": [1, 2, 6],
+        "col2": ["a", "b", "f"]
+    })
+    ds.write_dataset(t1, tempdir / "t1", format="parquet")
+    ds1 = ds.dataset(tempdir / "t1")
+
+    result = ds1.scanner(filter=pc.field("colA") < 3)
+    assert result.to_table() == pa.table({
+        "colA": [1, 2],
+        "col2": ["a", "b"]
+    })
diff --git a/python/pyarrow/tests/test_table.py b/python/pyarrow/tests/test_table.py
index bdbd2a7c8fc..81a8b27f4d7 100644
--- a/python/pyarrow/tests/test_table.py
+++ b/python/pyarrow/tests/test_table.py
@@ -2121,3 +2121,27 @@ def test_table_join_collisions():
         [10, 20, None, 99],
         ["A", "B", None, "Z"],
     ], names=["colA", "colB", "colVals", "colB", "colVals"])
+
+
+@pytest.mark.dataset
+def test_table_filter_expression():
+    t1 = pa.table({
+        "colA": [1, 2, 6],
+        "colB": [10, 20, 60],
+        "colVals": ["a", "b", "f"]
+    })
+
+    t2 = pa.table({
+        "colA": [99, 2, 1],
+        "colB": [99, 20, 10],
+        "colVals": ["Z", "B", "A"]
+    })
+
+    t3 = pa.concat_tables([t1, t2])
+
+    result = t3.filter(pc.field("colA") < 10)
+    assert result.combine_chunks() == pa.table({
+        "colA": [1, 2, 6, 2, 1],
+        "colB": [10, 20, 60, 20, 10],
+        "colVals": ["a", "b", "f", "B", "A"]
+    })