In today’s world, mental health concerns are on the rise, with stress and emotional imbalance significantly impacting individuals. The traditional stress and emotion detection methods are slow, intrusive, and lack real-time capabilities, making timely interventions difficult. In response, an innovative AI system based on WBANs processes data from multimodal sensors to sense stress and emotions in real time. The system processes data from multimodal physiological sensors, including heartbeat rate and electrodermal activity, using deep learning models such as CNN and LSTM networks for real-time applications. Experimental results show that the system achieves a classification accuracy of 94.1%, surpassing traditional models like CNN (82.5%) and LSTM (87.3%). The integration of edge computing minimizes energy consumption and latency, reducing total emotion detection time to 26 ms and making it highly efficient for wearable devices. Blockchain technology is incorporated to ensure privacy and accountability, providing security, transparency, and efficient data management. This approach offers a scalable and secure structure for continuous monitoring, with applications in healthcare, workplace environments, and personal health management, advancing real-time mental health interventions and potentially revolutionizing the field, instilling confidence in its secure and reliable operation.