Cardiovascular disease (CVD) continues to be a prominent issue in worldwide health, emphasizing the crucial importance of accurate forecasting and timely prevention. Machine learning (ML) has become a vital tool in the quest to improve CVD diagnosis. The present study aims to conduct a comparative analysis of various machine learning (ML) algorithms in terms of their performance, which includes Naïve Bayes, logistic regression, random forest, decision tree, artificial neural network, support vector machine, and XGBoost, in the prediction of CVD. Our results reveal that XGBoost outshines other models, achieving outstanding accuracy, precision, recall, and F-measure. Its exceptional ability to balance precision and recall makes it an excellent choice for the early identification of CVD. This study makes a valuable addition to the expanding field of study on CVD prediction. It underscores the significance of employing advanced ML algorithms that have the possibility to significantly influence public health outcomes.