Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3879 2019 2019 Computer Science College, University of Technology, 10066 Baghdad, Iraq Dena Dena Computer Science College, University of Technology, 10066 Baghdad, Iraq Bushra Fuaad‎‎ Khmas‎‎ Imam Al-Kadhum College (IKC), Iraq Amjed Abbas Ahmed Computer Science College, University of Technology, 10066 Baghdad, Iraq Ahmed T. Sadiq 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 2025 36 49 10.54216/JISIoT.170204 https://www.americaspg.com/articleinfo/18/show/3879