diff --git a/CHANGELOG.md b/CHANGELOG.md
index 47ac5c746..5a533241f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -156,6 +156,7 @@ Remember to align the itemized text with the first line of an item within a list
     automatically. Currently, NCCL 2.16 or newer is required.
   * We now provide Linux aarch64 wheels, both with and without NVIDIA GPU
     support.
+  * {meth}`jax.Array.item` now supports optional index arguments.
 
 * Deprecations
   * A number of internal utilities and inadvertent exports in {mod}`jax.lax` have
diff --git a/jax/_src/numpy/array_methods.py b/jax/_src/numpy/array_methods.py
index 7de5be2bc..fb1e52bd1 100644
--- a/jax/_src/numpy/array_methods.py
+++ b/jax/_src/numpy/array_methods.py
@@ -71,19 +71,12 @@ def _nbytes(arr: ArrayLike) -> int:
   return np.size(arr) * dtypes.dtype(arr, canonicalize=True).itemsize
 
 
-def _item(a: Array) -> Any:
+def _item(a: Array, *args) -> bool | int | float | complex:
   """Copy an element of an array to a standard Python scalar and return it."""
-  if dtypes.issubdtype(a.dtype, np.complexfloating):
-    return complex(a)
-  elif dtypes.issubdtype(a.dtype, np.floating):
-    return float(a)
-  elif dtypes.issubdtype(a.dtype, np.integer):
-    return int(a)
-  elif dtypes.issubdtype(a.dtype, np.bool_):
-    return bool(a)
-  else:
-    raise TypeError(a.dtype)
-
+  arr = core.concrete_or_error(np.asarray, a, context="This occurred in the item() method of jax.Array")
+  if dtypes.issubdtype(a.dtype, dtypes.extended):
+    raise TypeError(f"No Python scalar type for {a.dtype=}")
+  return arr.item(*args)
 
 def _itemsize(arr: ArrayLike) -> int:
   """Length of one array element in bytes."""
diff --git a/tests/lax_numpy_test.py b/tests/lax_numpy_test.py
index 9e83be82c..0774c24bc 100644
--- a/tests/lax_numpy_test.py
+++ b/tests/lax_numpy_test.py
@@ -3622,6 +3622,34 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):
     self._CheckAgainstNumpy(np_op, jnp_op, args_maker)
     self._CompileAndCheck(jnp_op, args_maker)
 
+  @jtu.sample_product(
+    shape=nonempty_array_shapes,
+    dtype=all_dtypes,
+    num_args=[0, 1, "all"],
+    use_tuple=[True, False]
+  )
+  def testItem(self, shape, dtype, num_args, use_tuple):
+    rng = jtu.rand_default(self.rng())
+    size = math.prod(shape)
+
+    if num_args == 0:
+      args = ()
+    elif num_args == 1:
+      args = (self.rng().randint(0, size),)
+    else:
+      args = tuple(self.rng().randint(0, s) for s in shape)
+    args = (args,) if use_tuple else args
+
+    np_op = lambda x: np.asarray(x).item(*args)
+    jnp_op = lambda x: jnp.asarray(x).item(*args)
+    args_maker = lambda: [rng(shape, dtype)]
+
+    if size != 1 and num_args == 0:
+      with self.assertRaises(ValueError):
+        jnp_op(*args_maker())
+    else:
+      self._CheckAgainstNumpy(np_op, jnp_op, args_maker)
+
   @jtu.sample_product(
     # Final dimension must be a multiple of 16 to ensure compatibilty of all dtype pairs.
     shape=[(0,), (32,), (2, 16)],