diff --git a/python/pyarrow/_compute.pyx b/python/pyarrow/_compute.pyx
index b0e3026b6f6..c1407e773d5 100644
--- a/python/pyarrow/_compute.pyx
+++ b/python/pyarrow/_compute.pyx
@@ -2441,6 +2441,19 @@ cdef class Expression(_Weakrefable):
         options = NullOptions(nan_is_null=nan_is_null)
         return Expression._call("is_null", [self], options)
 
+    def is_nan(self):
+        """
+        Check whether the expression is NaN.
+
+        This creates a new expression equivalent to calling the
+        `is_nan` compute function on this expression.
+
+        Returns
+        -------
+        is_nan : Expression
+        """
+        return Expression._call("is_nan", [self])
+
     def cast(self, type=None, safe=None, options=None):
         """
         Explicitly set or change the expression's data type.
diff --git a/python/pyarrow/array.pxi b/python/pyarrow/array.pxi
index dca99af8120..9deccfa1173 100644
--- a/python/pyarrow/array.pxi
+++ b/python/pyarrow/array.pxi
@@ -1261,6 +1261,16 @@ cdef class Array(_PandasConvertible):
         options = _pc().NullOptions(nan_is_null=nan_is_null)
         return _pc().call_function('is_null', [self], options)
 
+    def is_nan(self):
+        """
+        Return BooleanArray indicating the NaN values.
+
+        Returns
+        -------
+        array : boolean Array
+        """
+        return _pc().call_function('is_nan', [self])
+
     def is_valid(self):
         """
         Return BooleanArray indicating the non-null values.
diff --git a/python/pyarrow/table.pxi b/python/pyarrow/table.pxi
index e400605e566..5af381e6084 100644
--- a/python/pyarrow/table.pxi
+++ b/python/pyarrow/table.pxi
@@ -327,6 +327,30 @@ cdef class ChunkedArray(_PandasConvertible):
         options = _pc().NullOptions(nan_is_null=nan_is_null)
         return _pc().call_function('is_null', [self], options)
 
+    def is_nan(self):
+        """
+        Return boolean array indicating the NaN values.
+
+        Examples
+        --------
+        >>> import pyarrow as pa
+        >>> import numpy as np
+        >>> arr = pa.chunked_array([[2, np.nan, 4], [4, None, 100]])
+        >>> arr.is_nan()
+        <pyarrow.lib.ChunkedArray object at ...>
+        [
+          [
+            false,
+            true,
+            false,
+            false,
+            null,
+            false
+          ]
+        ]
+        """
+        return _pc().is_nan(self)
+
     def is_valid(self):
         """
         Return boolean array indicating the non-null values.
diff --git a/python/pyarrow/tests/test_compute.py b/python/pyarrow/tests/test_compute.py
index 24e9950f217..c3213ec3f03 100644
--- a/python/pyarrow/tests/test_compute.py
+++ b/python/pyarrow/tests/test_compute.py
@@ -35,6 +35,7 @@
 
 import pyarrow as pa
 import pyarrow.compute as pc
+from pyarrow.lib import ArrowNotImplementedError
 
 all_array_types = [
     ('bool', [True, False, False, True, True]),
@@ -1635,6 +1636,23 @@ def test_is_null():
     assert result.equals(expected)
 
 
+def test_is_nan():
+    arr = pa.array([1, 2, 3, None, np.nan])
+    result = arr.is_nan()
+    expected = pa.array([False, False, False, None, True])
+    assert result.equals(expected)
+
+    arr = pa.array(["1", "2", None], type=pa.string())
+    with pytest.raises(
+            ArrowNotImplementedError, match="has no kernel matching input types"):
+        _ = arr.is_nan()
+
+    with pytest.raises(
+            ArrowNotImplementedError, match="has no kernel matching input types"):
+        arr = pa.array([b'a', b'bb', None], type=pa.large_binary())
+        _ = arr.is_nan()
+
+
 def test_fill_null():
     arr = pa.array([1, 2, None, 4], type=pa.int8())
     fill_value = pa.array([5], type=pa.int8())