Machine learning is crucial in enhancing predictive and diagnostic capabilities across multiple sectors. Professionals can use it to identify potential conditions and assess the risks associated with different intervention strategies. Machine Learning methods have shown significant potential in enhancing disease detection by offering accurate, efficient, and automated diagnostic capabilities. Supervised machine learning is a widely used approach in artificial intelligence that enables systems to learn from labeled data and make accurate predictions. This paper explores various supervised learning techniques, including classification models, which are applied across diverse domains such as healthcare, finance, and natural language processing. This study focuses on the approaches and the applications of supervised learning and highlights its benefits, and discusses ongoing challenges and future directions for improving machine learning-based healthcare solutions.