With the increasing demand for machine learningin the medical field, this work addresses the growing need for accurate age prediction. Leveraging the NHANES dataset, which includes seven key features such as Body Mass Index and Glucose levels, the proposal utilizes advanced machine learning techniques to train a model that effectively predicts individuals’ ages. After exploring various predictive approaches, a model was selected that demonstrated superior performance, highlighting its potential to enhance healthcare planning, risk assessment, and personalized health interventions. This data-driven approach promises to improve healthcare outcomes and support strategicdecision-making in the medical domain.