This paper exemplifies how supervised machine learning can be used to predict three common chronic diseases (diabetes, hypertension, and stroke) utilizing clinical and health data from the real world. A comprehensive modeling pipeline was developed, encompassing data preprocessing, feature engineering, transformation, collinearity reduction, and class balancing. Logistic Regression, KNN, Random Forest, and XGBoost are considered as modeling techniques and used both cross-validation and holdout test sets. XGBoost demonstrated superior performance compared to other models, especially recall and AUC, while Logistic Regression served as a strong, interpretable baseline. Hypertension models achieved near-perfect results-likely due to clear class separability confirmed by PCA-and tree-based models improved stroke prediction by capturing complex nonlinear relationships. Within the predictor variables, many variables appeared within the models (regardless of the disease) and demonstrated commonality, supporting the development of possible integrated screening and pragmatic public health intervention with evidence to show how the data drove decisions.