In this thesis, a comprehensive understanding of supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, and Ensemble Stacking, is performed. This research also extends and further explores different feature selection techniques: correlation analysis, chi-squared, mutual information classification, and Recursive Feature Elimination (RFE). Then, a practical application in the context of coronary artery disease prediction was conducted to apply and analyze models' performance with different feature selection methods on various measures of accuracy, F1 score, and confusion matrix. The outcomes of this experimentation reveal that among models developed, Logistic Regression with chi-squared feature selection is a robust and reliable predictive model, achieving an accuracy of 87.65%. This research advances the understanding of machine learning algorithms and feature selection techniques, with practical implications for reliable prediction of coronary artery disease.