Epilepsy is a chronic neurological disorder characterized by recurrent clinical episodes stemming from various causes, which lead to significant physical, psychological, social, and intellectual challenges. In recent years, advancements in medical diagnostic techniques for epilepsy have become crucial for mitigating the risks associated with the disorder. Electroencephalography (EEG) remains the gold-standard method for seizure analysis as it captures changes in brain activity. However, individual variability in EEG signals, influenced by factors like age and alertness, makes the visual identification of epileptic abnormalities a complex, time-consuming, and error-prone task. This emphasizes the necessity for automated approaches to accurately analyze EEG signals. In this study, deep learning models—namely Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM)—were utilized to enhance seizure detection accuracy. Data from the UCI Center for Machine Learning and Intelligent Systems, comprising five classes, were employed for two experiments. Experiment 1 involved binary classification of classes 1 (epileptic) and 3 (non-epileptic). Experiment 2 aimed to distinguish between epileptic (class 1) and non-epileptic seizures by combining classes 2, 3, 4, and 5 into a non-epileptic category. The BiLSTM model demonstrated superior performance, achieving 98.00% accuracy in Experiment 2, significantly surpassing state-of-the-art methods. These findings highlight the robustness of BiLSTM in automated seizure detection, offering a reliable method for EEG signal analysis compared to conventional techniques.