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Modern digital entertainment platforms offer an overwhelming volume of content, leading to decision fatigue for users. This paper presents a hybrid AI-powered movie recommendation system that integrates deep learning, facial emotion recognition, and sentiment analysis to provide context-aware suggestions. Built using the Django framework and TensorFlow, the system utilizes DeepFace for real-time mood detection and TextBlob for analyzing user review sentiments. Experimental results demonstrate that the hybrid model achieves a Root Mean Square Error (RMSE) of 0.82, significantly outperforming traditional collaborative and content-based filtering methods. The integration of emotional context improved user satisfaction by 15%, offering a more personalized and immersive experience.