CardioVascular Disease (CVD) remains a foremost reason of morbidity and mortality worldwide, requiring advanced methods for initial finding and prevention of heart diseases. In recent years, the combination of Ensemble Machine Learning (EML) techniques with the Internet of Medical Things (IoMT) has developed as a capable possibility for improving CVD prediction. This research article provides a comprehensive review and analysis of the state-of-the-art techniques that are based on EML and IoMT for CVD prediction. The epidemiology and significance of CVD, followed by an overview of IoMT technologies and their potential in healthcare is presented. Subsequently, principles and methodologies of EML, including bagging, voting, and stacking techniques, highlighting their advantages in handling complex and heterogeneous healthcare data, are presented. Through a critical analysis of the literature, key trends, gaps, and future research directions in leveraging EML and IoMT for enhancing CVD prediction accuracy and clinical decision-making are identified.