Supervised and unsupervised learning are fundamental machine learning techniques. Supervised learning involves training models on labeled data, allowing them to predict outcomes based on input-output pairs. In contrast, unsupervised learning works with unlabeled data, enabling the discovery of hidden patterns and groupings without predefined labels. Both approaches play a crucial role in various fields like data mining, natural language processing, and computer vision, empowering AI systems to analyze data effectively and make intelligent decisions.