The escalating demand for timely and precise medical care, especially for the elderly, necessitates advanced technological interventions. This research delved into an edge-enabled cloud IoT system tailored for multi-disease health care, presenting a novel confluence of edge and cloud computing. Grounded in real-world scenarios, the system employed machine learning algorithms, including logistic regression, decision trees, and support vector machines, to predict heart, kidney, and brain diseases. Through a meticulously designed framework, real-time data processing at the edge was harmoniously integrated with in-depth analysis in the cloud. The results showcased the system’s potent capability to predict potential health concerns with significant accuracy. For the elderly demographic, the system transitioned health care from a reactive to a proactive paradigm, with continuous monitoring acting as a pivotal asset. The research serves as a beacon, highlighting the transformative potential of integrating advanced computing paradigms with health care, offering enhanced patient outcomes, especially for vulnerable groups. This study is not merely an exploration but a foundational step toward a future where technology and health care seamlessly intertwine, ensuring holistic well-being.