Cardiovascular disease (CVD) remains a global health threat. Accurately assessing CVD risk is crucial for preventative measures and interventions. This paper explores advancements in CVD risk prediction by examining several approaches and trials designed with the aim of diagnosing myocardial infarction (MI), which is generally known as a heart attack. MI is a critical medical condition in which a blocked artery cuts off blood flow and oxygen to a specific part of the heart muscle. This starves the heart tissue, leading to permanent damage and ranking as a top cause of mortality globally. By analyzing vast amounts of patient data, machine learning (ML) algorithms can predict the probability of a heart attack, pinpointing high-risk individuals. This allows for preventative measures and early intervention. The study in this paper utilizes tabular data for risk factors and includes an examination of multiple ML models to improve diagnostics, particularly for high-risk MI. These ML models include random forest classifiers, decision tree classifiers, support vector machines, logistic regression, and gradient boosting (GB). The experimental results reveal that GB has achieved higher accuracy than other models. This provides insights into enhancing cardiovascular health monitoring and diagnosis in clinical settings