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Heart diseases and related disorders have emerged as the most dangerous and leading cause of global deaths affecting mostly the elderly people who suffer from these diseases without realizing or knowing about it. Due to the fact that it’s very hard to notice the signs of the sickness at an early stage, mostly the signs will be shown once the heart problem has reached the peak level. This paper therefore presents a detailed review of how to monitor and predict heart disease using machine learning and the IoT. With the help of IoT, patients and doctors will monitor cardiovascular diseases early enough. A comparative analysis of different IoT technologies, most of which employed machine learning approaches for predicting and diagnosing cardiovascular disease, is conducted, different methodologies are compared and the results analysis is conducted and the performance tabulated. The Internet of Things (IoT) is transforming embedded systems into networked smart gadgets with sensors. The primary drawback of employing smart devices was their limited storage and processing capability, which cloud computing addressed by providing high-level processing and storage capacities. The Internet of Things (IoT) is transforming embedded systems into networked smart gadgets with sensors. The most significant disadvantage of using smart devices was their low cost. The problem of limited storage capacity and processing power was solved by cloud computing.