Cardiovascular (CVD) disease continues to pose a key global health challenge, emphasizing the need for accurate risk prediction and preventive measures. In recent decades, machine learning (ML) approaches have arisen as influential tools for analyzing complex medical datasets and enhancing CVD risk assessment. This book chapter presents a comprehensive review of recent advancements in ML-based CVD prediction, covering various ML algorithms, datasets, feature selection techniques, performance evaluation metrics, and associated challenges. The healthcare industry generates vast amounts of medical data, necessitating ML-driven decision-making for effective heart disease prediction. Recent research has explored the integration of multiple ML techniques to develop hybrid predictive models for improved accuracy. The proposed study employs data pre-processing techniques such as noise removal, handling missing values, and attribute classification to enhance classification and decision-making at different stages. The performance of the predictive model is assessed using classification metrics such as sensitivity, accuracy, and specificity. This chapter introduces a CVD prediction classification model designed to determine the likelihood of heart disease and raise awareness regarding early diagnosis. The proposed approach compares the predictive accuracy of decision tree, random forest, gradient boosting, and logistic regression by applying rule-based methodologies to regional datasets, ultimately identifying the most accurate model for CVD prediction.