The healthcare industry is undergoing a change thanks to the quick development of Big Data Analytics (BDA), which makes data-driven decisions that improve patient outcomes possible. Predictive modeling offers previously unheard-of possibilities for predicting health outcomes and identifying at-risk groups due to the massive volumes of data generated by the healthcare industry, including genomic information, wearable technology, and electronic health records (EHRs). This study examines how predictive analytics is being used in the healthcare industry, outlining strategies that use statistical and machine learning methods to better allocate resources, lower readmission rates, and foresee disease outbreaks. Even with its revolutionary potential, integrating BDA is fraught with difficulties, such as the requirement for standardized data formats, interoperability problems, and data protection issues. For predictive models to be successfully adopted, these obstacles must be removed. The paper explores the potential of predictive analytics in transforming healthcare from reactive to proactive, enhancing patient care and operational efficiency. It proposes the AAI-CDS framework, a structured approach to enhance clinical decision-making through AI, aiming for adaptability and integration into the healthcare landscape.