Purpose of Review <p>This review summarizes disparities in widely used breast cancer risk models. Historically, exclusion of diverse populations during model development has limited applicability. We examine how recalibration has improved these models and explore emerging tools for equitable risk prediction.</p> Recent Findings <p>The Gail model, Tyrer-Cuzick, and Breast Cancer Surveillance Consortium (BCSC) perform best in White women. The updated BCSC shows the most promise across groups, while the Black Women’s Health Study (BWHS) calculator is most accurate for Black women. Emerging tools such as polygenic risk scores (PRS) and artificial intelligence (AI) offer potential for more equitable predictions.</p> Summary <p>Except for the BWHS tool and BCSC, most calculators remain optimized for White women. PRS and AI may advance equity but require diverse data and inclusive validation to ensure broad clinical utility.</p>