This chapter examines supervised learning, a core machine learning paradigm where models learn from labeled examples to make predictions on new data. It covers the complete supervised learning workflow from data preparation to model deployment. The material first establishes mathematical foundations including probability theory, linear algebra, and optimization principles essential for understanding learning algorithms. Classification algorithms such as logistic regression, support vector machines, decision trees, and random forests are presented with their mathematical formulations and Python implementations. Regression techniques for predicting continuous values are explored through linear, polynomial, and regularized models. The chapter provides systematic approaches to model evaluation using appropriate metrics and validation strategies for ensuring generalization. Through hands-on exercises and real-world examples, readers develop practical skills in implementing supervised learning solutions while understanding their theoretical underpinnings.