aspg-18-3879-1792502950 20261020132910 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 17 2 Dena Kadhim Muhsen Computer Science College, University of Technology, 10066 Baghdad, Iraq Bushra Fuaad Khmas‎‎ Computer Science College, University of Technology, 10066 Baghdad, Iraq Amjed Abbas Ahmed Imam Al-Kadhum College (IKC), Iraq Ahmed T. Sadiq Computer Science College, University of Technology, 10066 Baghdad, Iraq This study investigates the application of AI-powered predictive analytics in chronic disease management, focusing on the most effective machine learning models for predicting patient risk and optimizing healthcare interventions, like Random Forest, Linear Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting were evaluated using a dataset of 10,000 patient records. The models were assessed based on their accuracy, interpretability, and clinical relevance. Gradient Boosting attained the highest predictive accuracy, with an AUC of 0.89. Random Forest followed closely with an AUC of 0.85, offering a good balance of accuracy and interpretability. Linear Regression, with an AUC of 0.75, demonstrated the trade-offs between simplicity and model performance, while SVM and KNN performed with AUCs of 0.82 and 0.78, respectively, with SVM being robust but facing scalability challenges and KNN being less practical for large datasets. These AI models improve patient outcomes, decrease healthcare costs, and optimize healthcare delivery. 2025 36 49 3879 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170204 https://www.americaspg.com/journal/18/article/3879 https://www.americaspg.com/storage/11749662577.pdf