Machine learning (ML) has emerged as a critical tool for enhancing cardiovascular disease (CVD) prediction, which is a leading cause of global mortality. This review systematically evaluates the state of ML techniques applied to CVD prediction, analyzing methodological trends, performance metrics, and data utilization patterns across 20 peer-reviewed studies published between August 2023 and December 2024. The review categorizes studies by methodology (deep learning, ensemble methods, traditional ML, and hybrid approaches), data types (tabular clinical data, ECG signals, multi-modal, and time-series), and performance metrics. Results indicate high predictive performance, with 75% of studies achieving accuracies above 98%. Deep learning and ensemble methods were the most common, contributing to the highest accuracy rates, with the Class-Incremental Deep and Broad Learning System (CIDBLS) achieving 100% accuracy. While tabular clinical data was predominant, multi-modal approaches demonstrated significant potential for holistic patient assessment. Innovations like hyperparameter optimization and class imbalance handling via SMOTE were also noted. Despite the promising results, limitations include inconsistent evaluation metrics, insufficient real-world validation, and lack of model interpretability. The review concludes that ML approaches for CVD prediction are mature and poised for clinical implementation, though further research is needed in model standardization, realtime processing, and explainability for broader clinical adoption. Keywords: Cardiovascular disease, machine learning, deep learning, ensemble methods, medical diagnosis, predictive modeling, clinical decision support, health informatics