Traditional healthcare systems have primarily focused on physical health, often overlooking mental well-being. With the rapid advancement of technology, integrating AI-driven emotion detection into mental health management can offer valuable insights. This paper presents a comprehensive mental health management system that utilizes Haar Cascade classifiers and a Keras deep learning model for real-time emotion recognition via OpenCV. A Flask-based web interface, built using HTML, CSS, and Python, enables users to monitor their emotional states and facilitates therapist booking and automated receipt generation. By leveraging facial expression analysis, the system provides a data-driven approach to mental health assessment, enabling early intervention. The platform also ensures accessibility and efficiency, reducing the burden on healthcare providers. Experimental evaluations demonstrate the system’s effectiveness in accurately detecting emotions and its potential in AI-assisted psychological support. Future enhancements will focus on multi-modal emotion detection, incorporating natural language processing (NLP) and IoT-based physiological monitoring for a more holistic approach to mental health assessment. This research contributes to the growing field of AI-powered mental health solutions, bridging the gap between technology and psychological well-being while promoting early detection and accessible care.