Deep learning models for epilepsy detection is a prominent research area that focuses on automatically identifying epileptic events from electroencephalogram (EEG) signals and other physiological recordings. Traditional detection methods rely on manual analysis by neurologists, which is time-consuming, subjective, and prone to errors, whereas deep learning models enable automated feature extraction, high-dimensional pattern recognition, and real-time monitoring. Early approaches employed convolutional neural networks (CNNs) to capture spatial patterns in EEG data, while recurrent neural networks (RNNs) and long short-term memory (LSTM) networks modeled temporal dependencies of seizure activity. Recent advances include hybrid CNN–LSTM architectures, attention-based networks, graph neural networks (GNNs) for modeling functional brain connectivity, and transformer-based models for sequence learning. Applications span seizure detection, classification of epileptic vs. non-epileptic EEG segments, patient monitoring, and aiding personalized treatment strategies. Current research also investigates data augmentation, transfer learning, robustness to noise and artifacts, lightweight models for wearable devices, and explainable AI for clinical interpretability, establishing deep learning as a critical tool for accurate, scalable, and efficient epilepsy detection.