This is a pretty common situation, and in most cases it comes down to overfitting, even if it doesn’t feel obvious at first.

What’s usually happening is the model is learning the training data too well, including noise or patterns that don’t generalize. Deep models have a lot of capacity, so they’ll happily memorize if you let them. High training accuracy with weak validation performance is often the first signal.

A few things I’d sanity-check based on experience:

• Data leakage: even small leaks (future info, target hints, improper scaling before the split) can inflate training results.

• Train/validation mismatch: different distributions, class balance shifts, or preprocessing applied inconsistently.

• Model complexity vs data size: deep models need more data than we often think. With limited data, simpler models sometimes do better.

• Regularization: lack of dropout, weight decay, or early stopping usually shows up exactly like this.

What’s helped me is treating validation performance as the truth, not the training curve. If validation is poor, I simplify first: fewer layers, stronger regularization, cleaner features. Only after that do I scale complexity back up.

In short, your model isn’t “failing”, it’s just telling you it has learned something too specific to the training set.