This project aims to develop a Retrieval-Augmented Generation (RAG) application designed to predict lung cancer risks using a comprehensive health dataset. The dataset includes critical features such as gender, age, smoking habits, anxiety, chronic diseases, and symptoms like coughing, wheezing, and chest pain. The RAG framework combines a robust retrieval system with generative AI to deliver precise and contextual insights. By analyzing input data, the application predicts the likelihood of lung cancer and provides evidence-based recommendations for early detection and intervention. Built on Groq and deployed via Streamlit, this solution empowers healthcare providers and individuals to make informed decisions.