This project explores predicting obesity levels using demographic, lifestyle, and physical attributes. Four machine learning models were applied: Multinomial Logistic Regression, Decision Trees, Random Forests, and K-Nearest Neighbors. Their performance was evaluated using confusion matrices and accuracy scores. Random Forest achieved the best overall performance, while Logistic Regression offered strong interpretability. The study highlights the potential of machine learning in classifying obesity levels and guiding health-related decision-making.