Cardiovascular disease (CVD) also very commonly called as “heart disease” is one of the supreme causes for the increasing mortality scale worldwide within the epochs of the decades. Data archeology is a universally used practice for dealing with complex and sizeable data in the healthcare domain. This experimental work applies several AIML algorithms, such as naïve Bayes, random forest, logistic regression, and light gradient-boosting machine. The ML models were applied to a benchmark dataset. According to the study, it is observed that out of these four machine learning algorithms, the naïve Bayes gives the least accuracy and specificity that is 87.60% and 84%, respectively, but in the case of sensitivity, logistic regression gives 90.20% which is lesser than other models. Random forest classifier is given maximum accuracy, sensitivity, and specificity that is 99%, 100%, and 98%, respectively, which is better than other models.