The prevalent neurological disorder known as epilepsy is characterized by periodic seizures brought on by atypical brain activity. This study presents a novel technique for identifying epileptic seizures using EEG signal analysis combined with a Convolutional Neural Network (CNN). The approach involves comprehensive preprocessing and effective image segmentation. The CNN architecture is designed to identify complex patterns within the EEG data. The model, trained with a consistent learning rate, achieves high validation accuracy. This method surpasses existing models by providing an efficient, streamlined approach that eliminates the need for extensive feature selection. Its high accuracy and ease of implementation make it a valuable tool in clinical settings, improving the timely and accurate diagnosis of epilepsy.