diff --git a/doc/whats_new/v1.2.rst b/doc/whats_new/v1.2.rst
index 70bd9b82d896d..1ce32968581a9 100644
--- a/doc/whats_new/v1.2.rst
+++ b/doc/whats_new/v1.2.rst
@@ -416,6 +416,10 @@ Changelog
   :pr:`18298` by :user:`Madhura Jayaratne <madhuracj>` and
   :user:`Guillaume Lemaitre <glemaitre>`.
 
+- |Fix| :class:`inspection.DecisionBoundaryDisplay` now raises error if input
+  data is not 2-dimensional.
+  :pr:`25077` by :user:`Arturo Amor <ArturoAmorQ>`.
+
 :mod:`sklearn.kernel_approximation`
 ...................................
 
diff --git a/sklearn/inspection/_plot/decision_boundary.py b/sklearn/inspection/_plot/decision_boundary.py
index 86836a81f7207..22b4590d9bc3c 100644
--- a/sklearn/inspection/_plot/decision_boundary.py
+++ b/sklearn/inspection/_plot/decision_boundary.py
@@ -6,7 +6,11 @@
 from ...utils import check_matplotlib_support
 from ...utils import _safe_indexing
 from ...base import is_regressor
-from ...utils.validation import check_is_fitted, _is_arraylike_not_scalar
+from ...utils.validation import (
+    check_is_fitted,
+    _is_arraylike_not_scalar,
+    _num_features,
+)
 
 
 def _check_boundary_response_method(estimator, response_method):
@@ -316,6 +320,12 @@ def from_estimator(
                 f"Got {plot_method} instead."
             )
 
+        num_features = _num_features(X)
+        if num_features != 2:
+            raise ValueError(
+                f"n_features must be equal to 2. Got {num_features} instead."
+            )
+
         x0, x1 = _safe_indexing(X, 0, axis=1), _safe_indexing(X, 1, axis=1)
 
         x0_min, x0_max = x0.min() - eps, x0.max() + eps
diff --git a/sklearn/inspection/_plot/tests/test_boundary_decision_display.py b/sklearn/inspection/_plot/tests/test_boundary_decision_display.py
index 8981c9d5a5e83..2d46507ab212c 100644
--- a/sklearn/inspection/_plot/tests/test_boundary_decision_display.py
+++ b/sklearn/inspection/_plot/tests/test_boundary_decision_display.py
@@ -38,6 +38,16 @@ def fitted_clf():
     return LogisticRegression().fit(X, y)
 
 
+def test_input_data_dimension():
+    """Check that we raise an error when `X` does not have exactly 2 features."""
+    X, y = make_classification(n_samples=10, n_features=4, random_state=0)
+
+    clf = LogisticRegression().fit(X, y)
+    msg = "n_features must be equal to 2. Got 4 instead."
+    with pytest.raises(ValueError, match=msg):
+        DecisionBoundaryDisplay.from_estimator(estimator=clf, X=X)
+
+
 def test_check_boundary_response_method_auto():
     """Check _check_boundary_response_method behavior with 'auto'."""