diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst
index 4c307d9e54250..0240ad706b6ca 100644
--- a/doc/whats_new/v1.1.rst
+++ b/doc/whats_new/v1.1.rst
@@ -503,6 +503,9 @@ Changelog
   messages when optimizers produce non-finite parameter weights. :pr:`22150`
   by :user:`Christian Ritter <chritter>` and :user:`Norbert Preining <norbusan>`.
 
+- |Enhancement| Adds :term:`get_feature_names_out` to
+  :class:`neural_network.BernoulliRBM`. :pr:`22248` by `Thomas Fan`_.
+
 :mod:`sklearn.pipeline`
 .......................
 
diff --git a/sklearn/neural_network/_rbm.py b/sklearn/neural_network/_rbm.py
index 6a6cb67f17de0..aac92c3108787 100644
--- a/sklearn/neural_network/_rbm.py
+++ b/sklearn/neural_network/_rbm.py
@@ -15,6 +15,7 @@
 
 from ..base import BaseEstimator
 from ..base import TransformerMixin
+from ..base import _ClassNamePrefixFeaturesOutMixin
 from ..utils import check_random_state
 from ..utils import gen_even_slices
 from ..utils.extmath import safe_sparse_dot
@@ -22,7 +23,7 @@
 from ..utils.validation import check_is_fitted
 
 
-class BernoulliRBM(TransformerMixin, BaseEstimator):
+class BernoulliRBM(_ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
     """Bernoulli Restricted Boltzmann Machine (RBM).
 
     A Restricted Boltzmann Machine with binary visible units and
@@ -284,6 +285,7 @@ def partial_fit(self, X, y=None):
                 self.random_state_.normal(0, 0.01, (self.n_components, X.shape[1])),
                 order="F",
             )
+            self._n_features_out = self.components_.shape[0]
         if not hasattr(self, "intercept_hidden_"):
             self.intercept_hidden_ = np.zeros(
                 self.n_components,
@@ -389,6 +391,7 @@ def fit(self, X, y=None):
             order="F",
             dtype=X.dtype,
         )
+        self._n_features_out = self.components_.shape[0]
         self.intercept_hidden_ = np.zeros(self.n_components, dtype=X.dtype)
         self.intercept_visible_ = np.zeros(X.shape[1], dtype=X.dtype)
         self.h_samples_ = np.zeros((self.batch_size, self.n_components), dtype=X.dtype)
diff --git a/sklearn/neural_network/tests/test_rbm.py b/sklearn/neural_network/tests/test_rbm.py
index aadae44479ad5..d36fa6b0bd11f 100644
--- a/sklearn/neural_network/tests/test_rbm.py
+++ b/sklearn/neural_network/tests/test_rbm.py
@@ -238,3 +238,15 @@ def test_convergence_dtype_consistency():
     )
     assert_allclose(rbm_64.components_, rbm_32.components_, rtol=1e-03, atol=0)
     assert_allclose(rbm_64.h_samples_, rbm_32.h_samples_)
+
+
+@pytest.mark.parametrize("method", ["fit", "partial_fit"])
+def test_feature_names_out(method):
+    """Check `get_feature_names_out` for `BernoulliRBM`."""
+    n_components = 10
+    rbm = BernoulliRBM(n_components=n_components)
+    getattr(rbm, method)(Xdigits)
+
+    names = rbm.get_feature_names_out()
+    expected_names = [f"bernoullirbm{i}" for i in range(n_components)]
+    assert_array_equal(expected_names, names)
diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
index fb4de8942f131..be26202d458d1 100644
--- a/sklearn/tests/test_common.py
+++ b/sklearn/tests/test_common.py
@@ -383,7 +383,6 @@ def test_pandas_column_name_consistency(estimator):
     "ensemble",
     "kernel_approximation",
     "preprocessing",
-    "neural_network",
 ]