In this chapter, it is mentioned that in the field of AI and data science, pattern recognition has emerged as a powerful tool for unraveling the complexities of complex data sets. This chapter delves into the core principles, techniques, algorithms, and diverse applications that underpin this transformative field. Pattern recognition empowers machines to identify and classify patterns within data. These patterns can manifest in various forms, ranging from sequential patterns like DNA sequences to spatial patterns like objects in images, statistical patterns like data distribution, rule-based patterns like medical diagnoses, and composite patterns that combine these elements. Effectively extracting valuable insights from data hinges on understanding the distinct characteristics of these diverse pattern types. Feature representation and extraction techniques play a crucial role in this process, transforming raw data into a suitable format that emphasizes relevant features for pattern identification. Classification, a cornerstone of pattern recognition, assigns data points to predefined categories. The chapter explores various supervised learning algorithms employed for classification. In essence, pattern recognition serves as a gateway to unlocking meaningful insights from data. By identifying patterns, we can gain a deeper understanding of the world around us and make informed decisions. As this field continues to evolve and refine its techniques, we can anticipate further advancements in the realm of data analysis.