This project uses EEG data (Muse headset, 30 participants) and AI ( CNN-LSTM) to detect depression. Features were extracted from different frequency bands (Delta, Theta, Alpha, Beta, Gamma). Models (KNN, Random Forest, SVM) were trained and tested; Random Forest achieved the highest accuracy (97.91%). Future work involves increasing dataset size and real-time processing.