Epilepsy, a neurological disorder characterized by recurrent seizures, remains a major challenge despite advances in pharmacotherapy. Conventional antiepileptic drugs (AEDs) exhibit suboptimal efficacy, necessitating the development of novel, more effective agents. This chapter explores computational approaches in drug discovery and their applications in identifying and optimizing AEDs. By employing techniques such as de novo drug design, homology modeling, molecular docking, molecular dynamics simulations, QSAR (quantitative structure-activity relationships), and receptor-based pharmacophore modeling, the mechanisms underlying antiepileptic activity are elucidated. Ligand- and structure-based computational methods enable high-throughput screening, facilitating the identification of lead compounds and potential molecular targets. Furthermore, advancements in molecular dynamics simulations and quantum mechanics aid in understanding drug-receptor interactions at an atomic level, enhancing the prediction of binding affinities and drug efficacy. Novel AEDs identified through these methodologies target diverse mechanisms, including modulation of gamma-aminobutyric acid (GABA) transmission, inhibition of voltage-gated ion channels, and interactions with NMDA receptors. This chapter underscores the significance of computational techniques in addressing the unmet need for safe and effective AEDs, providing insights into the future of antiepileptic drug discovery.