diff --git a/crates/burn-core/src/nn/loss/binary_cross_entropy.rs b/crates/burn-core/src/nn/loss/binary_cross_entropy.rs
index f645c84fd9..54b80f4f60 100644
--- a/crates/burn-core/src/nn/loss/binary_cross_entropy.rs
+++ b/crates/burn-core/src/nn/loss/binary_cross_entropy.rs
@@ -118,9 +118,9 @@ impl<B: Backend> BinaryCrossEntropyLoss<B> {
             (targets_float.neg() + 1.) * logits.clone() - log_sigmoid(logits)
         } else {
             // - (target * log(input) + (1 - target) * log(1 - input))
-            (targets_float.clone() * logits.clone().log()
-                + (targets_float.neg() + 1.) * (logits.neg() + 1.).log())
-            .neg()
+            // https://github.com/tracel-ai/burn/issues/2739: clamp at -100.0 to avoid undefined values
+            (targets_float.clone() - 1) * logits.clone().neg().log1p().clamp_min(-100.0)
+                - targets_float * logits.log().clamp_min(-100.0)
         };
 
         if let Some(weights) = &self.weights {
@@ -171,6 +171,38 @@ mod tests {
     use crate::tensor::{activation::sigmoid, TensorData};
     use crate::TestBackend;
 
+    #[test]
+    fn test_binary_cross_entropy_preds_all_correct() {
+        let device = Default::default();
+        let preds = Tensor::<TestBackend, 1>::from_floats([1.0, 0.0, 1.0, 0.0], &device);
+        let targets =
+            Tensor::<TestBackend, 1, Int>::from_data(TensorData::from([1, 0, 1, 0]), &device);
+
+        let loss_actual = BinaryCrossEntropyLossConfig::new()
+            .init(&device)
+            .forward(preds, targets)
+            .into_data();
+
+        let loss_expected = TensorData::from([0.000]);
+        loss_actual.assert_approx_eq(&loss_expected, 3);
+    }
+
+    #[test]
+    fn test_binary_cross_entropy_preds_all_incorrect() {
+        let device = Default::default();
+        let preds = Tensor::<TestBackend, 1>::from_floats([0.0, 1.0, 0.0, 1.0], &device);
+        let targets =
+            Tensor::<TestBackend, 1, Int>::from_data(TensorData::from([1, 0, 1, 0]), &device);
+
+        let loss_actual = BinaryCrossEntropyLossConfig::new()
+            .init(&device)
+            .forward(preds, targets)
+            .into_data();
+
+        let loss_expected = TensorData::from([100.000]); // clamped value
+        loss_actual.assert_approx_eq(&loss_expected, 3);
+    }
+
     #[test]
     fn test_binary_cross_entropy() {
         // import torch