Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3599 2018 2018 Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt Nada Nada Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt Mohamed Handosa Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt M. Z. Rashad Accurate emotion detection is crucial for individuals facing communication barriers, yet existing approaches struggle with real-time limitations and information Individual privacy. This research presents a new IoT-based framework that integrates EEG and physiological signals from wearable sensors with deep learning models, including CNN, Decision Trees, SVM, KNN, and Naïve Bayes. Unlike traditional methods, our approach effectively mitigates data latency and sensor noise while ensuring compliance with GDPR and HIPAA standards. Experimental results demonstrate a validated accuracy of 99-100%, outperforming state-of-the-art models. These developments establish our framework as a game-changing instrument for affective computing applications, enhancing human-machine interaction and healthcare quality of life. 2025 2025 144 163 10.54216/FPA.190113 https://www.americaspg.com/articleinfo/3/show/3599