Heart illness, also designated as cardiovascular illness, wraps up several disorders that influence the cardiovascular system and is the leading foundation of mortality globally throughout the duration of the previous few decades. Patients with heart diseases are increasing quickly as a result of poor consumption habits and a lack of health knowledge. Heart disease affects and kills approximately one out of every four people. In healthcare, especially in the discipline of cardiology, early and effective detection of cardiac disease is crucial. Giving patients the right therapy depends on reliable and exact identification of cardiac problems. Because Machine Learning (ML) systems can find patterns in data, their usage in the healthcare sector has increased dramatically. Patients and medical personnel may all profit from a decreased chance of misdiagnosis when ML algorithms are used to identify cardiac problems. The intent of this paper is to implement ML models to analyze health decision-making using heart disease data. This work implements four ML models namely Gaussian NB., RF, DT, and SVC to analyze the medical data collected from the UCI repository. With an accuracy rate of 98%, the RF model is the finest performer, according to the data.