Deep learning models for epilepsy prediction is a rapidly growing research area that focuses on leveraging neural networks to detect and forecast epileptic seizures from electroencephalogram (EEG) signals and other physiological data. Traditional methods rely on manual interpretation of EEG recordings by experts, which is time-consuming, subjective, and prone to errors, whereas deep learning enables automated feature extraction, temporal pattern recognition, and real-time analysis. Early research employed convolutional neural networks (CNNs) for spatial feature extraction from EEG signals, while recurrent neural networks (RNNs) and long short-term memory (LSTM) networks captured temporal dependencies in seizure activity. Recent advances include hybrid CNN–LSTM architectures, attention mechanisms, graph neural networks (GNNs) to model brain connectivity, and transformer-based models for sequence modeling. Applications span real-time seizure prediction, detection of preictal states, patient monitoring, and personalized intervention strategies. Current studies also explore data augmentation, transfer learning, lightweight models for wearable devices, and robustness against noise and artifacts in EEG signals, establishing deep learning as a key technology for accurate, efficient, and predictive epilepsy management.