The incorporation of Artificial Intelligence (AI) into healthcare has opened up a transformative era for predictive analytics in disease diagnosis and treatment. By harnessing large volumes of medical data, AI-powered predictive models utilize sophisticated machine learning and deep learning methods to detect patterns and forecast health outcomes. This technology not only improves the accuracy of diagnoses but also facilitates early disease detection and tailors treatment plans to individual patients, ultimately enhancing patient care and making healthcare delivery more efficient. AI systems draw from varied data sources such as electronic health records (EHRs), medical imaging, and genetic profiles, offering a holistic view of patient health. However, the adoption of AI in healthcare is not without obstacles, including concerns over data privacy, the necessity for extensive high-quality datasets, and the challenge of seamlessly integrating AI tools into current clinical processes. This abstract provides an overview of the advancements in AI-driven healthcare predictive analytics, outlines significant achievements, and examines the hurdles and future prospects for its application in diagnosing and treating diseases. By overcoming these challenges, AI holds the promise to revolutionize healthcare by making it more predictive, accurate, and personalized.