diff --git a/src/lightning/pytorch/callbacks/model_checkpoint.py b/src/lightning/pytorch/callbacks/model_checkpoint.py
index 6b7b2831a2e04..117d6bc77020e 100644
--- a/src/lightning/pytorch/callbacks/model_checkpoint.py
+++ b/src/lightning/pytorch/callbacks/model_checkpoint.py
@@ -338,6 +338,13 @@ def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModul
                 self._save_topk_checkpoint(trainer, monitor_candidates)
             self._save_last_checkpoint(trainer, monitor_candidates)
 
+    @override
+    def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+        """Ensure save_last=True is applied when training ends."""
+        if self.save_last and not self._last_checkpoint_saved:
+            monitor_candidates = self._monitor_candidates(trainer)
+            self._save_last_checkpoint(trainer, monitor_candidates)
+
     @override
     def state_dict(self) -> dict[str, Any]:
         return {
diff --git a/tests/tests_pytorch/checkpointing/test_model_checkpoint.py b/tests/tests_pytorch/checkpointing/test_model_checkpoint.py
index 7b17498865889..5f9781cc4a1b2 100644
--- a/tests/tests_pytorch/checkpointing/test_model_checkpoint.py
+++ b/tests/tests_pytorch/checkpointing/test_model_checkpoint.py
@@ -1666,3 +1666,30 @@ def val_dataloader(self) -> DataLoader:
     trainer_kwargs["max_epochs"] = 4
     trainer = Trainer(**trainer_kwargs, callbacks=ModelCheckpoint(**mc_kwargs))
     trainer.fit(model, ckpt_path=checkpoint_path)
+
+
+def test_save_last_without_save_on_train_epoch_and_without_val(tmp_path):
+    """Test that save_last=True works correctly when save_on_train_epoch_end=False in a model without validation."""
+
+    # Remove validation methods to test the edge case
+    model = BoringModel()
+    model.validation_step = None
+    model.val_dataloader = None
+
+    checkpoint_callback = ModelCheckpoint(
+        dirpath=tmp_path,
+        save_last=True,
+        save_on_train_epoch_end=False,
+    )
+
+    trainer = Trainer(
+        max_epochs=2,
+        callbacks=[checkpoint_callback],
+        logger=False,
+        enable_progress_bar=False,
+    )
+
+    trainer.fit(model)
+
+    # save_last=True should always save last.ckpt
+    assert (tmp_path / "last.ckpt").exists()