Electroencephalography (EEG) is a fundamental tool for monitoring brain activity and is widely used in the diagnosis and management of epilepsy. With the growing prevalence of epilepsy and the limitations of manual EEG interpretation, artificial intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL), have gained prominence in automating seizure detection, classification, and prediction. This review provides a comprehensive synthesis of AI‐based approaches applied to EEG signals for epileptic seizure analysis. We examine 86 peer‐reviewed studies from 2014 to 2023 and analyze eleven publicly available EEG datasets, highlighting their characteristics, applications, and implications for model performance. Key components of the pipeline, including feature extraction methods, model architectures (e.g., CNNs, LSTMs, and hybrids), and evaluation strategies, are critically reviewed. The paper further categorizes methodologies based on their use of spatial, temporal, and hybrid learning, as well as their capacity for patient‐specific versus patient‐independent prediction. In addition to summarizing current achievements, this survey identifies persistent challenges such as interpatient variability, lack of standardized evaluation metrics, poor model generalization across datasets, and limited real‐time deployment. It also outlines future directions, including data‐centric approaches, transfer learning, multimodal fusion, and explainable AI, all of which are crucial for translating academic research into clinically viable solutions. By critically analyzing the strengths, limitations, and opportunities in the field, this review aims to guide future research toward the development of robust, interpretable, and deployable AI systems for epileptic seizure monitoring, ultimately improving clinical decision‐making and patient outcomes.
Tadesse et al. (Thu,) studied this question.