Accurate and precise detection of heart diseases is crucial in providing correct treatment to patients. The application of machine learning (ML) algorithms has increased in the medical niche due to their ability to recognize patterns from the data. Implementation of ML algorithms in the detection of heart diseases can mitigate the risks of misdiagnosis, which can be of great help to the patients as well as the diagnosticians. Heart disease could be of different types, such as coronary heart disease and cardiovascular disease. In this article, relevant literature related to ML techniques provides valuable insights into detecting, and classifying heart diseases that aim to provide reasonable solutions for making health decisions. In addition to it, four ML algorithms, namely Gaussian NB, support vector classification (SVC), decision tree (DT), and random forest (RF), have been implemented to detect heart diseases on a dataset obtained from the University of California Irvine machine learning (UCI) repository. A comparison between the performance of these techniques is then carried out where the SVC, Gaussian NB, DT, and RF worked with an accuracy of 85%, 87%, 95%, and 96%, respectively. The results show that random forest is the best choice for this dataset.