The Smart Health Monitoring and Disease Detection System leverages IoT-based sensors and machine learning algorithms to deliver continuous and real-time health monitoring, using sensors like a camera, pulse oximeter, and temperature sensor, it captures vital health, including nail discoloration, tongue abnormalities, skin disease, and fever detection. The collected data is processed through five machine learning models Extreme Gradient Boosting, Random Forest, K-Nearest Neighbors, Convolutional Neural Networks, and Linear Regression which are compared to identify the most accurate for each health metric. In particular, Convolutional Neural Networks excel in visual data analysis for nail and tongue abnormalities, while Extreme Gradient Boosting demonstrates superior accuracy in complex pattern detection, particularly for fever diagnosis. Processed data is transmitted to cloud servers for analysis and abnormal patterns automated SMS alerts, enabling timely intervention. A mobile application provides real-time monitoring, to access historical data and includes an interactive AI assistant that offers instant feedback based on user-reported symptoms. This system aims to enhance proactive healthcare and reduce healthcare costs.