Healthcare disparities persist across various demographics, resulting in unequal access to disease management and treatment results. This work investigates using predictive modeling with the AdaBoost algorithm to detect and address these disparities successfully. Utilizing a varied dataset including demographic, socioeconomic, and clinical factors, a strong prediction model that identifies at-risk populations inside the healthcare system is constructed. AdaBoost, recognized for its efficacy in augmenting prediction accuracy via ensemble learning, was used to refine the classification of patients according to their probability of encountering undesirable health consequences. The results demonstrate that including AdaBoost in predictive analytics may markedly enhance the detection of healthcare inequities, allowing healthcare professionals to execute targeted treatments. Moreover, the approach emphasizes the need to address socioeconomic determinants of health to mitigate disparities in illness treatment. Utilizing data-driven insights enables healthcare systems to optimize resource allocation and personalize guidelines to address varied populations' different needs, enhancing health equality and results. © 2025 IEEE.