Supervised learning and unsupervised learning are two key approaches in machine learning, each with distinct methodologies. Supervised learning involves training a model on labeled data, where the algorithm learns from input-output pairs to make predictions. In contrast, unsupervised learning works with unlabeled data, where the model identifies patterns and structures on its own. Understanding these differences is crucial for choosing the right approach for your data-driven projects.