Heart disease, a global ailment with substantial mortality rates, poses a significant health concern. The prevalence of heart disease has escalated due to the demanding nature of contemporary occupations and inherent genetic predispositions. Hence, timely detection of cardiac disorders is paramount to preserving lives. However, the analysis of routine clinical data presents a formidable challenge in identifying cardiovascular ailments. Leveraging machine learning approaches to scrutinize clinical data can furnish effective solutions for informed decision-making and precise prognostications. This research endeavors to predict heart disease by examining the data of 303 individuals encompassing 14 distinct categories. Several machine learning methodologies, namely K-Nearest Neighbor, Gaussian Naive Bayes, Logistic Regression, Random Forest, Gradient Boosting, and Artificial Neural Networks, are proposed as potential remedies to address the problem. The experimental findings unveil that Gradient Boosting attains a remarkable accuracy of 95% and Artificial Neural Networks exhibit a commendable accuracy of 90.1%, establishing them as the most successful models in this study. These results underscore the superior performance of the proposed techniques vis-à-vis the existing literature.