diff --git a/src/lightning/pytorch/CHANGELOG.md b/src/lightning/pytorch/CHANGELOG.md
index 99af24fe80b69..c75c82127afac 100644
--- a/src/lightning/pytorch/CHANGELOG.md
+++ b/src/lightning/pytorch/CHANGELOG.md
@@ -58,6 +58,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `LightningCLI` loading of hyperparameters from `ckpt_path` failing for subclass model mode ([#21246](https://github.com/Lightning-AI/pytorch-lightning/pull/21246))
 
 
+- Fixed how `ThroughputMonitor` calculated training time ([#21291](https://github.com/Lightning-AI/pytorch-lightning/pull/21291))
+
+
 ---
 
 ## [2.5.5] - 2025-09-05
diff --git a/src/lightning/pytorch/callbacks/throughput_monitor.py b/src/lightning/pytorch/callbacks/throughput_monitor.py
index b88ee90bde38d..d38928d33de75 100644
--- a/src/lightning/pytorch/callbacks/throughput_monitor.py
+++ b/src/lightning/pytorch/callbacks/throughput_monitor.py
@@ -109,7 +109,9 @@ def _start(self, trainer: "Trainer") -> None:
         stage = trainer.state.stage
         assert stage is not None
 
-        if stage not in self._samples:
+        reset_needed = trainer.state.fn == TrainerFn.FITTING or stage not in self._samples
+
+        if reset_needed:
             self._throughputs[stage].reset()
             self._lengths[stage] = 0
             self._samples[stage] = 0
@@ -202,10 +204,17 @@ def on_validation_batch_end(
     def on_validation_end(self, trainer: "Trainer", *_: Any) -> None:
         if trainer.sanity_checking or trainer.state.fn != TrainerFn.FITTING:
             return
+
+        train_times = self._throughputs[RunningStage.TRAINING]._time
+        val_times = self._throughputs[RunningStage.VALIDATING]._time
+
+        train_elapsed = train_times[-1] if train_times else 0.0
+        val_elapsed = val_times[-1] if val_times else 0.0
+
         # add the validation time to the training time before continuing to avoid sinking the training throughput
-        training_finished = self._t0s[RunningStage.TRAINING] + sum(self._throughputs[RunningStage.TRAINING]._time)
+        training_finished = self._t0s[RunningStage.TRAINING] + train_elapsed
         time_between_train_and_val = self._t0s[RunningStage.VALIDATING] - training_finished
-        val_time = sum(self._throughputs[RunningStage.VALIDATING]._time)
+        val_time = val_elapsed
         self._t0s[RunningStage.TRAINING] += time_between_train_and_val + val_time
 
     @override
diff --git a/tests/tests_pytorch/callbacks/test_throughput_monitor.py b/tests/tests_pytorch/callbacks/test_throughput_monitor.py
index 7dda7875a43c7..93bfe4e844c3a 100644
--- a/tests/tests_pytorch/callbacks/test_throughput_monitor.py
+++ b/tests/tests_pytorch/callbacks/test_throughput_monitor.py
@@ -420,3 +420,65 @@ def variable_batch_size_fn(batch):
             train_samples.append(metrics["train/samples"])
         elif "train|samples" in metrics:
             train_samples.append(metrics["train|samples"])
+
+
+def test_throughput_monitor_validation_with_many_epochs(tmp_path):
+    """Ensure ThroughputMonitor handles many epochs with validation and time increases monotonically."""
+
+    logger_mock = Mock()
+    logger_mock.save_dir = tmp_path
+    monitor = ThroughputMonitor(batch_size_fn=lambda x: 1)
+    model = BoringModel()
+    model.flops_per_batch = 10
+    num_epochs = 100
+
+    trainer = Trainer(
+        devices=1,
+        logger=logger_mock,
+        callbacks=[monitor],
+        max_epochs=num_epochs,
+        limit_train_batches=2,
+        limit_val_batches=1,
+        log_every_n_steps=1,
+        enable_checkpointing=False,
+        enable_model_summary=False,
+        enable_progress_bar=False,
+    )
+
+    timings = []
+    t = 0.0
+    for _ in range(num_epochs):
+        timings += [
+            t,  # train batch 1 start
+            t + 3.0,  # train batch 1 end and start batch 2
+            t + 6.0,  # train batch 2 end
+            t + 7.0,  # val start
+            t + 8.0,  # val end
+        ]
+        t += 10.0
+
+    with mock.patch("time.perf_counter", side_effect=timings):
+        try:
+            trainer.fit(model)
+        except Exception as e:
+            pytest.fail(f"ThroughputMonitor raised an unexpected exception: {e}")
+
+    start_train_timings_idx, end_train_timings_idx = 0, 1
+    batch_num = 1
+    cur_train = timings[end_train_timings_idx] - timings[start_train_timings_idx]
+    for c in logger_mock.log_metrics.mock_calls:
+        metrics = getattr(c, "kwargs", None) or {}
+        metrics = metrics.get("metrics", metrics)
+        for k, v in metrics.items():
+            if k.endswith("train/time"):
+                assert v == cur_train, f"Expected train/time {cur_train}, got {v}"
+                if batch_num == 1:
+                    start_train_timings_idx += 1
+                    end_train_timings_idx += 1
+                    batch_num = 2
+                else:
+                    start_train_timings_idx += 3
+                    end_train_timings_idx += 3
+                    batch_num = 1
+                if end_train_timings_idx < len(timings):
+                    cur_train += timings[end_train_timings_idx] - timings[start_train_timings_idx]