Integration of IoT with machine learning is changing the entire scenario of healthcare by introducing real-time and predictive health monitoring solutions. In this research work, a full-scale health monitoring system is being designed that acquires data using IoT sensors and predicts heart diseases accurately using robust machine learning algorithms. The physiological data, which includes pulse rate and body temperature, are preprocessed in real-time using effective normalization and outlier removal techniques, thereby making it reliable. Machine learning classifiers, including SVM, Naïve Bayes, and Random Forest, were implemented and tested on the dataset. SVM came out with an accuracy of 86%. A total of 40 samples dataset was used for validation of the system's performance in real-time applications. Results indicated that the system could mitigate latency issues and scalability, hence suitable for multiple healthcare settings especially in resource-limited areas. Key findings proved the effectiveness of the system to enhance early diagnosis and personalized care. Recommendations include increasing data security, integration of edge computing, expanded diagnostic capabilities, and large-scale testing for increased adaptability. This research works toward the advancement of healthcare technology by providing a scalable, efficient, and reliable framework for predictive health monitoring.