The rapid advancement of technology has paved the way for innovative solutions in healthcare, with smart health monitoring systems emerging as a transformative approach to improve patient care and early diagnosis. This paper presents a Smart Health Monitoring System (SHMS) leveraging machine learning algorithms to analyze real-time data from wearable devices and other IoT-enabled health sensors. The system is designed to monitor vital parameters such as heart rate, blood pressure, oxygen saturation, body temperature, and physical activity levels. By utilizing advanced machine learning techniques such as classification, regression, and anomaly detection, the SHMS can predict potential health risks, detect abnormalities, and provide timely alerts to patients and healthcare providers. The proposed system integrates cloud-based platforms for secure data storage and advanced analytics, ensuring scalability and accessibility. It also incorporates user-friendly mobile and web applications for real-time visualization of health metrics, personalized insights, and recommendations for lifestyle improvements. Experimental results demonstrate the effectiveness of the system in accurately diagnosing conditions like arrhythmias, hypertension, and early signs of chronic diseases. The SHMS also facilitates preventive healthcare by promoting proactive monitoring, reducing hospital visits, and enabling remote patient care.

Smart Health Monitoring, Machine Learning, IoT, Wearable Devices, Predictive Analytics, Remote Patient Care.