diff --git a/keras/src/metrics/reduction_metrics.py b/keras/src/metrics/reduction_metrics.py
index 3dde46f9583..b4c075e2f62 100644
--- a/keras/src/metrics/reduction_metrics.py
+++ b/keras/src/metrics/reduction_metrics.py
@@ -199,6 +199,9 @@ def __init__(self, fn, name=None, dtype=None, **kwargs):
             self._direction = "down"
 
     def update_state(self, y_true, y_pred, sample_weight=None):
+        y_true = backend.cast(y_true, self.dtype)
+        y_pred = backend.cast(y_pred, self.dtype)
+
         mask = backend.get_keras_mask(y_pred)
         values = self._fn(y_true, y_pred, **self._fn_kwargs)
         if sample_weight is not None and mask is not None:
diff --git a/keras/src/metrics/reduction_metrics_test.py b/keras/src/metrics/reduction_metrics_test.py
index f697918ccd3..679bed08180 100644
--- a/keras/src/metrics/reduction_metrics_test.py
+++ b/keras/src/metrics/reduction_metrics_test.py
@@ -1,6 +1,9 @@
 import numpy as np
 
 from keras.src import backend
+from keras.src import layers
+from keras.src import metrics
+from keras.src import models
 from keras.src import testing
 from keras.src.backend.common.keras_tensor import KerasTensor
 from keras.src.metrics import reduction_metrics
@@ -174,3 +177,17 @@ def test_weighted_dynamic_shape(self):
             KerasTensor((None, 5)),
         )
         self.assertAllEqual(result.shape, ())
+
+    def test_binary_accuracy_with_boolean_inputs(self):
+        inp = layers.Input(shape=(1,))
+        out = inp > 0.5
+        model = models.Model(inputs=inp, outputs=out)
+
+        x = np.random.rand(32, 1)
+        y = x > 0.5
+
+        res = model.predict(x)
+        metric = metrics.BinaryAccuracy()
+        metric.update_state(y, res)
+        result = metric.result()
+        assert result == 1.0