Supervised and unsupervised machine learning are two fundamental types of AI learning models. Supervised machine learning uses labeled data, where the algorithm learns from input-output pairs to make predictions. For example, spam email detection is a supervised learning application. In contrast, unsupervised machine learning works with unlabeled data, identifying patterns and relationships without predefined categories. Clustering customer segments for targeted marketing is an example. Supervised learning is typically more accurate for specific tasks, while unsupervised learning excels in exploring unknown patterns. Both play crucial roles in AI, depending on the availability of labeled data and the nature of the problem.