aspg-31-4434-1791686922 20261011024842 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 2026 8 1 S. Phani Praveen Associate Professor, Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada – 520007, Andhra Pradesh, India Massila Kamalrudin Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia Sai Vellela Associate Professor, Department of CSE – Data Science, Chalapathi Institute of Technology, Guntur – 522016, Andhra Pradesh, India Deshinta Arrova Dewi Professor, Faculty of Data Science and Information Technology (FDSIT), INTI International University, Malaysia Dedeepya Pulletikurthy Department of Computer Science & Engineering, SRM University AP, Amaravati, Andhra Pradesh, India Vahiduddin Shariff Department of CSE, Sir C. R. Reddy College of Engineering, Eluru, Andhra Pradesh, India Rapid growth in the adoption of Electronic Health Records (EHRs), Internet of Medical Things (IoMT) devices, wearable sensor technology, and digital healthcare systems offers immense scope for intelligent healthcare decision support. However, most AI-enabled healthcare systems in use today still lack explainability, contextual reasoning capabilities, and effective decision-making. For these reasons, this research develops an Agentic Generative AI Framework for Intelligent Disease Prediction and Decision-Making in smart healthcare. The framework incorporates predictive analytics, Generative AI-based clinical reasoning, and autonomous intelligent agents into a coherent healthcare framework. Six specific agents are used for data gathering, data analysis, disease prediction, clinical reasoning, treatment recommendations, and patient monitoring. The combined functionality of these agents supports disease prediction, clinical reasoning, and personalized treatment plans. Evaluation was performed on healthcare datasets related to heart disease, diabetes, chronic kidney disease, and breast cancer. Experimental results show high efficiency, stable accuracy across diseases, reliable recommendation generation, and enhanced healthcare intelligence compared with traditional ML, DL, and LLM methods. Results show that combining Agentic AI with Generative AI increases explainability, adaptability, and efficiency in medical decision support. The proposed model represents an encouraging path toward intelligent, patient-centered, and explainable smart healthcare systems. 2026 33 41 4434 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAACI.080105 https://www.americaspg.com/journal/31/article/4434 https://www.americaspg.com/storage/articles/manuscripts/01M332KCYGK7ZGQ3690YN05AZ9.pdf