Epilepsy, a prevalent neurological disorder characterized by abnormal neuronal activity leading to seizures, presents significant challenges in healthcare. Electroencephalography (EEG) has emerged as a cornerstone in epilepsy diagnosis, capturing intricate brainwave patterns. While Deep Learning (DL) algorithms have revolutionized seizure analysis, their opacity hinders clinical adoption. This comprehensive review explores the integration of Explainable Artificial Intelligence (XAI) techniques to enhance DL model interpretability in epilepsy diagnosis. Our analysis highlights attention mechanisms and Shapley Additive Explanations (SHAP) as pivotal XAI tools for elucidating DL decision-making processes. These techniques empower clinicians to identify critical EEG features, and biomarkers associated with seizures, fostering deeper insights into epilepsy pathophysiology. Future research directions encompass hybrid XAI frameworks, extensive clinical validation studies, and ethical considerations to ensure responsible deployment. Incorporating XAI enhances the interpretability and reliability of AI systems, leading to improved patient outcomes in epilepsy care.