Systemic Lupus Erythematosus (SLE), commonly known as lupus, is a chronic autoimmune disease in which the immune system attacks healthy tissues of the body. Early detection of lupus is difficult because symptoms vary widely among individuals and often resemble other diseases. This research presents a Lupus Risk Prediction System using Machine Learning that analyzes various medical and lifestyle factors to estimate the risk of lupus. The proposed system uses parameters such as age, gender, ANA level, ESR level, UV exposure, fatigue score, stress index, sleep hours, family history, and rash symptoms. Machine learning algorithms are applied to analyze these health indicators and predict lupus risk levels. The system is implemented using the Random Forest Classifier and deployed through a Flask-based web application, allowing users to enter their health parameters and obtain real-time lupus risk predictions categorized as Low Risk, Moderate Risk, or High Risk. The system aims to assist healthcare awareness and early disease risk assessment.