After the COVID-19 pandemic, more and more people are choosing to self-diagnose online instead of seeing a doctor in person. This trend has led to the meteoric rise in popularity of several online self-diagnosis tools. Users can input their symptoms to access health information through these tools. However, depending only on these systems carries with it significant risks. Many people may experience difficulties interpreting their results due to their inability to comprehend medical information they come across online, such as in blogs or notes. This presents an opportunity to enhance awareness and instruct individuals on how to proficiently explore health information. This research project employs cloud computing and machine learning to provide an innovative method for monitoring the health of cardiac patients in the cloud. The objective of this study is to discover compelling solutions to the stated issues. Two critical factors to consider when assessing an individual’s risk of developing cardiovascular disease are their comprehension of the circumstances and their ability to anticipate potential outcomes. Various machine learning methods are used to achieve this, including support vector machine, K-nearest neighbors, neural networks, logistic regression, and gradient boosting trees. Google Cloud Firebase does extensive testing on both the algorithm and its accompanying mobile application. The collection contains both recognized and unfamiliar user inputs. People can monitor their health and make informed decisions about self-diagnosis with the help of this technology. The primary objective of this chapter is to enhance patient outcomes by facilitating a more reliable and precise assessment of heart disease risk.