Human gait recognition is a vital biometric modality for surveillance, identity and verification, human-computer interaction, with applications ranging from security systems to healthcare monitoring. Traditional approaches, however, often face challenges in terms of accuracy, robustness, and computational efficiency. This research proposes an innovative approach for human gait recognition by combining deep learning with enhanced Ant Colony Optimization (ACO) to improve feature extraction, classification, and recognition accuracy.