Machine learning intervention exists in almost all the domains of science and technology and the field of medicine is not an exception. Disease diagnosis and treatment are the two prime processes of disease management. Machine learning algorithms are of varied nature and have the competency to handle large volumes of data. The primary aim of this research is to explore the applications of supervised learning algorithms in diagnosing cardiovascular diseases. The prediction model developed in this work facilitates identifying the disease patterns with huge volumes of historical patient data and big data analytics is used to deal with the data sets. Cardiovascular diseases encompass a wide range of disorders affecting the functioning of the heart and cause the death of several millions of people. The early detection of these diseases based on complex data sets is very essential in making diagnosis more accurate. This research work stands distinct with the significance of making disease diagnosis with 1000 samples using various kinds of machine learning algorithms by considering the wide range of input features. The efficiency of this machine-based diagnosis is determined by making a relative analysis with other algorithms. The results of sensitivity analysis are in favor of the support vector machine with a precision of 0.89, accuracy of 0.91, and false positive rate of 0.00926. Thus, the prediction model proposed in this research work will certainly assist medical practitioners in making optimal disease diagnosis and treatment. However, there are a few limitations to the current study as the model’s performance is highly dependent on the quality and diversity of the biological dataset used.