aspg-3-3599-1791417845 20261008000405 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 19 1 Nada Asar 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 Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt 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 144 163 3599 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.190113 https://www.americaspg.com/journal/3/article/3599 https://www.americaspg.com/storage/11739927524.pdf