diff --git a/src/lightning/pytorch/CHANGELOG.md b/src/lightning/pytorch/CHANGELOG.md
index 863a3a4a7e939..e58f6c669ad5a 100644
--- a/src/lightning/pytorch/CHANGELOG.md
+++ b/src/lightning/pytorch/CHANGELOG.md
@@ -52,6 +52,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed preventing recursive symlink creation iwhen `save_last='link'` and `save_top_k=-1` ([#21186](https://github.com/Lightning-AI/pytorch-lightning/pull/21186))
 
 
+- Fixed `last.ckpt` being created and not linked to another checkpoint ([#21244](https://github.com/Lightning-AI/pytorch-lightning/pull/21244))
+
 ---
 
 ## [2.5.5] - 2025-09-05
diff --git a/src/lightning/pytorch/callbacks/model_checkpoint.py b/src/lightning/pytorch/callbacks/model_checkpoint.py
index fc83c0a4513a2..8a5d9dcdf786f 100644
--- a/src/lightning/pytorch/callbacks/model_checkpoint.py
+++ b/src/lightning/pytorch/callbacks/model_checkpoint.py
@@ -380,7 +380,11 @@ def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModu
             monitor_candidates = self._monitor_candidates(trainer)
             if self._every_n_epochs >= 1 and (trainer.current_epoch + 1) % self._every_n_epochs == 0:
                 self._save_topk_checkpoint(trainer, monitor_candidates)
-            self._save_last_checkpoint(trainer, monitor_candidates)
+            # Only save last checkpoint if a checkpoint was actually saved in this step or if save_last="link"
+            if self._last_global_step_saved == trainer.global_step or (
+                self.save_last == "link" and self._last_checkpoint_saved
+            ):
+                self._save_last_checkpoint(trainer, monitor_candidates)
 
     @override
     def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
@@ -397,7 +401,11 @@ def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModul
 
             if self._every_n_epochs >= 1 and (trainer.current_epoch + 1) % self._every_n_epochs == 0:
                 self._save_topk_checkpoint(trainer, monitor_candidates)
-            self._save_last_checkpoint(trainer, monitor_candidates)
+            # Only save last checkpoint if a checkpoint was actually saved in this step or if save_last="link"
+            if self._last_global_step_saved == trainer.global_step or (
+                self.save_last == "link" and self._last_checkpoint_saved
+            ):
+                self._save_last_checkpoint(trainer, monitor_candidates)
 
     @override
     def on_exception(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", exception: BaseException) -> None:
diff --git a/tests/tests_pytorch/checkpointing/test_model_checkpoint.py b/tests/tests_pytorch/checkpointing/test_model_checkpoint.py
index d2bbea7ecdafe..feeb9a55b91d9 100644
--- a/tests/tests_pytorch/checkpointing/test_model_checkpoint.py
+++ b/tests/tests_pytorch/checkpointing/test_model_checkpoint.py
@@ -2124,3 +2124,59 @@ def test_save_last_without_save_on_train_epoch_and_without_val(tmp_path):
 
     # save_last=True should always save last.ckpt
     assert (tmp_path / "last.ckpt").exists()
+
+
+def test_save_last_only_when_checkpoint_saved(tmp_path):
+    """Test that save_last only creates last.ckpt when another checkpoint is actually saved."""
+
+    class SelectiveModel(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.validation_step_outputs = []
+
+        def validation_step(self, batch, batch_idx):
+            outputs = super().validation_step(batch, batch_idx)
+            epoch = self.trainer.current_epoch
+            loss = torch.tensor(1.0 - epoch * 0.1) if epoch % 2 == 0 else torch.tensor(1.0 + epoch * 0.1)
+            outputs["val_loss"] = loss
+            self.validation_step_outputs.append(outputs)
+            return outputs
+
+        def on_validation_epoch_end(self):
+            if self.validation_step_outputs:
+                avg_loss = torch.stack([x["val_loss"] for x in self.validation_step_outputs]).mean()
+                self.log("val_loss", avg_loss)
+                self.validation_step_outputs.clear()
+
+    model = SelectiveModel()
+
+    checkpoint_callback = ModelCheckpoint(
+        dirpath=tmp_path,
+        filename="best-{epoch}-{val_loss:.2f}",
+        monitor="val_loss",
+        save_last=True,
+        save_top_k=1,
+        mode="min",
+        every_n_epochs=1,
+        save_on_train_epoch_end=False,
+    )
+
+    trainer = Trainer(
+        max_epochs=4,
+        callbacks=[checkpoint_callback],
+        logger=False,
+        enable_progress_bar=False,
+        limit_train_batches=2,
+        limit_val_batches=2,
+        enable_checkpointing=True,
+    )
+
+    trainer.fit(model)
+
+    checkpoint_files = list(tmp_path.glob("*.ckpt"))
+    checkpoint_names = [f.name for f in checkpoint_files]
+    assert "last.ckpt" in checkpoint_names, "last.ckpt should exist since checkpoints were saved"
+    expected_files = 2  # best checkpoint + last.ckpt
+    assert len(checkpoint_files) == expected_files, (
+        f"Expected {expected_files} files, got {len(checkpoint_files)}: {checkpoint_names}"
+    )