Epileptic seizure detection is a critical task in neurological diagnosis, where timely identification can significantly improve patient outcomes. This work presents a hybrid deep learning model that combines Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (Bi-LSTM) networks for analyzing EEG signals. The CNN component captures spatial characteristics of brain activity, while the Bi-LSTM layer models temporal dependencies in both forward and backward directions. The proposed model is evaluated using the Bonn EEG dataset, achieving an accuracy of 96.09%. The results indicate that the hybrid approach performs better than conventional machine learning techniques such as Support Vector Machines and Random Forests, making it suitable for automated seizure detection systems.