aspg-2-3972-1791417039 20261007235039 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2026 17 1 Ali Subhi Alhumaima Electronic Computer Centre, University of Diyala, Diyala, Iraq Waleed Khalid Al-Zubaidi Electronic Computer Centre, University of Diyala, Diyala, Iraq El-Sayed M. El-Kenawy Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt Applied Science Research Center. Applied Science Private University, Amman, Jordan Marwa M. Eid In this study, we present an integrated approach to IoT-based environmental data analysis using a collection of unsupervised-learning techniques. We employed KMeans clustering in particular to identify natural groupings in environmental and behavioral features such as air quality, noise level, temperature, stress level, sleeping hours, and mood score. We then trained a Decision Tree classifier to predict and interpret cluster membership from raw sensor readings. The data of more than 30,000 observations in indoor school environments has multifaceted relationships between environmental factors and psychological well-being. KMeans consistently detected three environmental-behavioral states, and the Decision Tree classifier performed 87% classification accuracy, which indicated extremely high predictability power in addition to interpretability. The results indicated that sleep duration, air, and stress were the main factors for cluster discrimination. The hybrid model introduces the potential of observing real-time environmental and mental states for applications in smart cities. The approach is scalable, interpretable, and usable in IoT settings for proactivity-enabled wellness management. 2026 10 20 3972 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.170102 https://www.americaspg.com/journal/2/article/3972 https://www.americaspg.com/storage/31753558532.pdf