Cardiovascular disease is a major contributor to global mortality. Machine learning models have demonstrated promising capabilities for accurately predicting heart disease through the analysis of extensive datasets. Machine learning came out to be a popular apparatus used in analyzing and forecasting various health issues, including heart disease. The aim of this report is to discuss various approaches and techniques used in forecasting cardiovascular disease using machine learning algorithms. Data collection as well as preprocessing, machine learning algorithms, and evaluation metrics are discussed in detail. However, there are obstacles that arise when working with machine learning for heart illness prediction, for instance availability of exceptional datasets while interpretation of complex models. This report provides recommendations for improving heart disease prediction, such as the use of appropriate feature selection techniques and visualization to aid interpretation. The report also covers various equipment learnedness algos, such as logistic regression, support vector machines, decision trees, and random forests, that are used to predict heart disease having high accuracy. Evaluation metrics, covering about use of accuracy, precision, recall, and F1- score, are frequently employed in machine learning, are also discussed in detail. However, there are challenges associated with using equipment learnedness for cardiovascular illness forecasting, including its availability of premium datasets or selecting the appropriate features. Some more research is required to develop more robust project. Furthermore, this report will discuss about challenges faced while using machine learning algorithms for early detection of cardiovascular illness and provide recommendations on the future research.