Background: GLOBOCAN data suggest that breast cancer has the highest burden in Indian women with an age-adjusted incidence (25.8) and death (12.7) per 100,000 women rate which is slated to rise further. The rising burden of breast cancer is unabated due to the lack of population risk data, awareness, targeted interventions, late detection, and management. To the best of our knowledge, this is the first attempt at developing a risk prediction tool for Breast cancer in India. There is a glaring need to explore modifiable Breast cancer risk factors in India with an intent to educate the public to enhance risk awareness, screening uptake, risk reduction, early detection, and improved clinical outcomes. Objectives: The study aims the identification of possible risk factors for Breast cancer and variables affecting differential cancer incidence in rural and urban populations in India. The development of a machine learning-based risk prediction tool will help in continuous data integration for accurate Breast cancer risk profile assessment and can be expanded for uses across India. Methods: A matched case-control study will be done where cases will be enrolled from those getting registered at National Cancer Institute (NCI) and controls will be selected from preidentified urban and rural community cohorts. The project will be implemented in two phases where initial data i.e. socio-demographic, personal, environmental, clinical profile, laboratory, etc. of those with (cases, N= 500) and without breast cancer (controls, N= 1000) will be collected. In the next phase, a tool would be developed and internally validated for the identification of significant risk factors using a machine learning tool. Subsequently, as a future direction, the tool will be integrated into an open IT (webpage, m-health app) platform for application in a real-world field setting for application in community cohorts for risk surveillance and external validation. Expected outcome: The study will assess the prevalence of possible risk factors and their association with Breast cancer. Additionally, the Identification of socio-epidemiological factors linked to differential incidence rates of breast cancer in urban and rural populations in India by developing a UI (user interface) risk calculation model embedding continuous data integration process into machine learning algorithms for improved accuracy.