Epilepsy is a chronic neurological disorder affecting approximately 50 million people worldwide. Accurate and efficient detection of epileptic seizures is crucial for effective treatment and management. Electroencephalogram (EEG) signals, being non-invasive and rich in temporal information, are widely used for epilepsy diagnosis. However, manual inspection of EEG data is time-consuming and relies heavily on the expertise of clinicians. Machine learning techniques offer promising solutions for automating the classification of epileptic and non-epileptic EEG signals. In this study, we investigate the performance of various machine learning models – including Light Gradient Boosting Machine, deep learning architectures, and convolutional neural networks (CNN)—in classifying EEG signals for epilepsy detection. Our experiments demonstrate that CNN outperform other models due to their ability to capture complex spatial and temporal patterns inherent in EEG data. The CNN model achieved higher accuracy and better convergence, as evidenced by the confusion matrix and learning curves. In contrast, Deep Neural Networks without convolutional layers showed lower performance, likely due to their limitations in capturing the intricate features of EEG signals. Similarly, the Light Gradient Boosting Machine model exhibited good initial results but failed to generalize well to unseen data, possibly due to overfitting and lack of convergence. These findings highlight the potential of CNN-based approaches in the automated recognition of epileptic seizures using EEG signals, paving the way for more efficient and accurate diagnostic tools.