### Motivation and Paper Contributions  
This review paper is motivated by the urgent need to assemble the role of machine learning methods in improving cardiovascular disease prediction, which is to date considered a critical area of public health concern. With the continuous evolution of machine learning techniques and the growing public health impact of cardiovascular disease, this paper seeks to provide an up-to-date and comprehensive evaluation of machine learning techniques for cardiovascular disease prediction. This paper bridges the gap between machine learning and cardiology, emphasizing the importance of interdisciplinary collaboration and domain knowledge. The scope of this paper encompasses a thorough analysis of various machine learning models, feature selection and engineering, evaluation metrics, recent advances, and their public health impact. This paper carries out a discussion encompassing various aspects of machine learning models, including their underlying mechanisms, applications, strengths, and limitations. Moreover, this paper offers an insight \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Cardiovascular Disease Type** & **Description** \\ \hline CAD & CAD occurs when the blood vessels (coronary arteries) that supply the heart with oxygen and nutrients become narrowed or blocked due to the buildup of cholesterol and fatty deposits (atherosclerosis). \\ \hline Heart failure & Occurs when the heart cannot pump blood effectively, leading to a reduced supply of oxygen and nutrients to the body’s tissues and organs. It can result from CAD, hypertension, and heart valve diseases. \\ \hline Arrhythmias & This condition involves problems with one or more of the heart’s valves. It can lead to valve stenosis (narrowing) or regurgitation (leakage). Common valve disorders include aortic stenosis and mitral regurgitation. \\ \hline Valvular & This condition involves problems with one or more of the heart’s valves. It can lead to valve stenosis (narrowing) or regurgitation (leakage). Common valve disorders include aortic stenosis and mitral regurgitation. \\ \hline Cardiomyopathy & This is related to the muscle and can weaken the heart’s ability to pump blood effectively. It can be inherited or acquired, and there are different types, such as dilated cardiomyopathy and hypertrophic cardiomyopathy. \\ \hline Congenital & This is present at birth and involves structural abnormalities in the heart. It can affect the heart’s walls, valves, or blood vessels. \\ \hline Infective Endocarditis & This is an infection of the inner lining of the heart (endocardium) and the heart valves. It is typically caused by bacteria or other microorganisms that enter the bloodstream and settle in the heart. \\ \hline Vascular & This refers to conditions or disorders that affect the blood vessels, which include arteries and veins throughout the body, which can disrupt the normal flow of blood. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of major cardiovascular disease types and a description of each type. ful review, emphasizing the innovative applications of machine learning in cardiovascular disease diagnosis. This paper provides an overview of the recent advancements in the application of machine learning for cardiovascular disease, aiming to elucidate the current trends, approaches, and challenges associated with machine learning techniques in cardiovascular disease diagnosis. What sets this review apart is its unique focus on the incorporation of domain knowledge, up-to-date coverage of trends, and the holistic view of the public health impact of machine learning in cardiovascular disease prediction. This paper aims to be an effective resource for researchers and specialists facilitating informed decision-making and fostering advancements in the field. Furthermore, the contents of this review paper are structured to provide a clear and organized presentation of the role of machine learning in cardiovascular disease prediction.