This chapter explores the analytical process of mining high utility patterns from utility databases, emphasizing the significance and extraction of meaningful patterns based on their utility. It introduces key concepts, such as utility calculation for items and patterns within transactions, and discusses the challenge of large search space in pattern mining. Techniques like the EFIM algorithm are highlighted to discover high utility patterns efficiently. Additionally, the chapter extends to high utility frequent pattern mining, which integrates both utility and support constraints to refine pattern discovery by excluding infrequent but high utility patterns. The HUIM algorithm is also demonstrated with a practical Python implementation, providing a robust framework for mining utility databases and uncovering critical insights for real-world applications.