An Information Retrieval System (IRS) is designed to store, organize, retrieve, and maintain information in response to user queries. Unlike traditional database systems that rely on structured data and exact matching, an IRS focuses on retrieving relevant information from large collections of unstructured or semi-structured data such as text, images, audio, video, and other multimedia content. With the rapid growth of the Internet and advances in low-cost computing and storage technologies, information retrieval systems have become essential tools for managing vast digital repositories and enabling efficient access to knowledge. The primary objective of an IRS is to reduce the user’s effort in locating needed information. This effort, known as information retrieval overhead, includes query formulation, execution, examination of retrieved results, and reading non-relevant items. To evaluate system effectiveness, two key performance measures are used: precision, which reflects the accuracy of retrieved results, and recall, which measures the completeness of retrieval. A balance between these measures is crucial for effective information access. Modern information retrieval systems support natural language queries, allowing users to express their information needs in everyday language. Internally, an IRS operates through several functional processes, including item normalization, selective dissemination of information, document database search, and index database search. Item normalization converts diverse data formats into standardized, searchable representations through processes such as zoning, token identification, and stop-word removal. Indexing and automatic file-building techniques further enhance retrieval efficiency.