The goal of this paper is to explore the relationship between various factors like smoking habits and diabetes history and the likelihood of developing heart disease. Using the heart disease dataset from the UCI Machine Learning Repository, this paper applies predictive analysis through building different models and methods. By examining the relationships between different attributes and the presence of heart disease, this paper aims to build a model that can predict heart disease risk based on patient attributes. In this paper we built predictive models using logistic regression, decision trees, support vector machines, random forests, and gradient boosting algorithms. Our findings showed that the Random Forest model emerged as the best performing model with high accuracy and robustness.