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Mental health disorders represent a growing concern in contemporary society, often remaining undiagnosed due to the subtlety of their symptoms and the stigma surrounding psychological conditions. This project, titled Mental Health Prediction Using Natural Language Sentiment and Facial Expression Recognition, presents a comprehensive, automated framework that leverages advancements in natural language processing and computer vision to identify potential indicators of mental health disturbances. The proposed system adopts a dual-modality approach by analysing both textual data and facial expressions to assess emotional and psychological states. Sentiment analysis is performed on user-generated textual input, extracting emotional cues and evaluating the polarity and intensity of sentiments expressed. Simultaneously, facial expression recognition is employed to decode non-verbal signals using facial landmarks and emotion classification models. By integrating insights from these two modalities, the system enhances the reliability & depth of mental health assessment. This methodology aims to assist mental health professionals by providing a non-invasive, real-time, and scalable solution.