Artificial intelligence, particularly machine learning (ML), has great potential in improving patient outcomes through clinical decision support systems. ML has the capability to revolutionize patient care by improving diagnostics, treatment personalization, and operational efficiency. This article focuses on the evolution of supervised learning models and their applications, including classification and regression techniques. Challenges such as data quality, ethical concerns, model bias, and privacy issues are discussed, alongside the importance of human–AI collaboration.