A leading cause of chronic illnesses like diabetes and cardiovascular disorders, obesity is a serious global health concern. The goal of this research is to create a predictive framework for determining an individual’s level of obesity based on their physical characteristics and eating habits. The suggested model provides excellent accuracy and resilience by utilizing ensemble learning approaches, particularly Random Forest and XGBoost classifiers. The dataset contains important variables that are analyzed to properly predict obesity levels, including BMI, frequency of physical activity, eating habits, and alcohol intake. Healthcare professionals can identify at-risk individuals and create focused intervention programs thanks to the system’s insightful information. The results demonstrate the model’s ability to produce accurate forecasts, paving the way for its use in useful healthcare applications to facilitate individualized intervention strategies.