The worldwide prevalence of heart disease is on the rise, emphasizing the urgency for early detection methods and preventive actions. This research investigates the feasibility of employing different statistical models to anticipate the onset of heart disease by examining crucial health markers and lifestyle variables. By leveraging data from the University College Irvine (UCI) Dataset, which includes comprehensive patient attributes, researchers aim to develop a predictive framework. Three distinct Machine Learning Classifier Models – Logistic Regression, K-Nearest Neighbors, and Random Forest – are employed to construct this predictive model. Initial sections of the paper focus on identifying crucial clinical features associated with heart disease, while subsequent segments evaluate the accuracy of the predictive models on the dataset. Through this approach, the study aims to contribute to the early diagnosis and prevention of cardiovascular diseases, utilizing advanced data mining and machine learning techniques.