diff --git a/doc/whats_new/v1.2.rst b/doc/whats_new/v1.2.rst
index 3874e10c03b26..66772f23b172f 100644
--- a/doc/whats_new/v1.2.rst
+++ b/doc/whats_new/v1.2.rst
@@ -380,7 +380,9 @@ Changelog
 
 - |Feature| :func:`metrics.roc_auc_score` now supports micro-averaging
   (`average="micro"`) for the One-vs-Rest multiclass case (`multi_class="ovr"`).
-  :pr:`24338` by :user:`Arturo Amor <ArturoAmorQ>`.
+
+- |Fix| :func: `metrics.ndcg_score` and `metrics.dcg_score` will raise error if `y_true` is a single value.
+  :pr:`24482` by :user:`Madi Ebersole <mae5357>`.
 
 :mod:`sklearn.model_selection`
 ..............................
diff --git a/sklearn/metrics/_ranking.py b/sklearn/metrics/_ranking.py
index 3fea49b1e285c..161021a60f036 100644
--- a/sklearn/metrics/_ranking.py
+++ b/sklearn/metrics/_ranking.py
@@ -1379,6 +1379,12 @@ def _tie_averaged_dcg(y_true, y_score, discount_cumsum):
     return (ranked * discount_sums).sum()
 
 
+def _check_dcg_target_length(y_true):
+    """Check that y_true has the correct shape for DCG."""
+    if len(y_true) == 1 and len(y_true[0]) == 1:
+        raise ValueError("Cannot compute DCG for a single sample.")
+
+
 def _check_dcg_target_type(y_true):
     y_type = type_of_target(y_true, input_name="y_true")
     supported_fmt = (
@@ -1492,6 +1498,7 @@ def dcg_score(
     y_true = check_array(y_true, ensure_2d=False)
     y_score = check_array(y_score, ensure_2d=False)
     check_consistent_length(y_true, y_score, sample_weight)
+    _check_dcg_target_length(y_true)
     _check_dcg_target_type(y_true)
     return np.average(
         _dcg_sample_scores(
@@ -1541,6 +1548,7 @@ def _ndcg_sample_scores(y_true, y_score, k=None, ignore_ties=False):
     dcg_score : Discounted Cumulative Gain (not normalized).
 
     """
+
     gain = _dcg_sample_scores(y_true, y_score, k, ignore_ties=ignore_ties)
     # Here we use the order induced by y_true so we can ignore ties since
     # the gain associated to tied indices is the same (permuting ties doesn't
@@ -1660,6 +1668,8 @@ def ndcg_score(y_true, y_score, *, k=None, sample_weight=None, ignore_ties=False
             " raise a ValueError on negative y_true values starting from version 1.4.",
             FutureWarning,
         )
+    # raise value error if y_true or y_score is single input
+    _check_dcg_target_length(y_true)
     _check_dcg_target_type(y_true)
     gain = _ndcg_sample_scores(y_true, y_score, k=k, ignore_ties=ignore_ties)
     return np.average(gain, weights=sample_weight)
diff --git a/sklearn/metrics/tests/test_ranking.py b/sklearn/metrics/tests/test_ranking.py
index 827c145ed01dc..0848d8c47853e 100644
--- a/sklearn/metrics/tests/test_ranking.py
+++ b/sklearn/metrics/tests/test_ranking.py
@@ -1746,6 +1746,14 @@ def _test_dcg_score_for(y_true, y_score):
     assert ideal == pytest.approx((np.sort(y_true)[:, ::-1] / discount).sum(axis=1))
 
 
+def _test_dcg_score_errors():
+    # raise error if y_true is single value
+    y_true = np.array([1])
+    y_score = np.array([1])
+    with pytest.raises(ValueError, match="Cannot compute DCG for a single sample."):
+        dcg_score(y_true, y_score)
+
+
 def test_dcg_ties():
     y_true = np.asarray([np.arange(5)])
     y_score = np.zeros(y_true.shape)
@@ -1859,6 +1867,14 @@ def _test_ndcg_score_for(y_true, y_score):
     assert score.shape == (y_true.shape[0],)
 
 
+def _test_ndcg_score_errors():
+    # raise error if y_true is single value
+    y_true = np.array([1])
+    y_score = np.array([1])
+    with pytest.raises(ValueError, match="Cannot compute DCG for a single sample."):
+        ndcg_score(y_true, y_score)
+
+
 def test_partial_roc_auc_score():
     # Check `roc_auc_score` for max_fpr != `None`
     y_true = np.array([0, 0, 1, 1])