Cardiovascular diseases (CVDs) are considered as significant global health issue as they bear the brunt of the world’s morbidity and mortality. This research paper aims to provide a comprehensive analysis of cardiovascular illnesses’ causes, warning indications, and available treatments. The multifactorial nature of CVDs, which encompass various diseases for example heart failure, stroke, coronary artery diseases, and peripheral vascular disorders, is explained in the first section of the study. A thorough analysis of the pathophysiological mechanisms behind diverse disorders sheds light on the complex interplay of genetic, behavioral, and environmental factors impacting the emergence of certain disorders. In this study, we have assessed and contrasted various machine learning-based techniques including random forest (RF), logistic regression (LR), support vector machine (SVM), and neural networks in order to predict cardiovascular diseases. Researchers and healthcare professionals can gain valuable insights into the applicability and efficacy of each strategy by carefully examining the advantages, limitations, and associated performance indicators of each method.