This study explores the potential of machine learning (ML) models for analyzing public health data related to obesity and diabetes. The paper investigates the effectiveness of various ML algorithms in predicting an individual’s susceptibility to the chronic diseases. The analysis leverages two publicly available datasets: one on obesity levels in Latin American countries and another on diabetes within the Pima Indian population. The employed ML models achieve high accuracy in obesity prediction (85.79–90.44%), with light gradient boosting machine (LGBM) and eXtreme gradient boosting (XGBoost) demonstrating a slight edge over Random Forest and Support Vector Machine (SVM). For diabetes prediction, the accuracy ranges from 70.13 to 76.62%, likely reflecting the inherent complexity of the disease. LGBM again exhibits the highest base accuracy for both obesity and diabetes prediction. The evaluation using precision, recall, and F1-Score metrics suggests a well-balanced performance by the models, effectively identifying true cases while minimizing false positives. This highlights the potential of ML for early disease detection and intervention in public health.