A new coronavirus has prompted countries around the world to emphasize health care. In addition to sensory equipment, technology plays a vital part in healthcare in the form of communication, recording and display devices.Monitoring numerous medical indicators and post-operative days is crucial. This has enabled the most recent IoT-based trend in healthcare communication. In previous ten years, there has been a great deal of technology advancement and healthcare services-related research. More specifically, the Internet of Things (IoT) has demonstrated potential use in connecting different medical equipment, sensors, and healthcare professionals to deliver high-quality clinical services in a remote region.On the other hand Machine Learning (ML) has gain lot of attention in recent times due to its accurate performance. Hence, in this paper Machine learning based IoTenabled healthcare system is presented to detect and monitor the heart disease condition and health condition of a patient.As a result, the healthcare sector will operate more efficiently, lower healthcare costs, boost accessibility to healthcare services, and improve patient safety.In this approach four different ML algorithms namely Decision Tree (DT), K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) and their performance is assessed in terms of accuracy, precision, F1-score and Recall.