NLP is now a crucial technology for automatically extracting information from huge amounts of text data. This review looks at how NLP techniques are implemented in Document Management Systems (DMS) to improve their effectiveness and precision in managing unstructured and semi-structured documents. The paper examines the development of NLP from initial rule-based strategies to contemporary deep learning models, focusing on essential techniques like Named Entity Recognition (NER), syntactic and dependency parsing, and sentiment analysis which form the basis of information extraction. Incorporating these methods into DMS enables the automation of tasks like document indexing, categorization, and metadata creation, resulting in notable enhancements in operational efficiency and decision-making precision. Various examples from different sectors like healthcare, legal services, finance, and human resources are examined, showcasing the real-world advantages and obstacles of combining NLP with DMS. In spite of these benefits, the review points out various obstacles that need to be dealt with. These include the intricacy of natural language, worries about data privacy, and the requirement for scalable and adjustable solutions that cater to specific industry requirements. The summary provides guidance on upcoming research areas, highlighting the need for stronger, context-sensitive NLP models and more adaptable integration frameworks for existing DMS systems. This review offers a thorough examination of the present status of NLP in document organization, highlighting its potential for transformation and the obstacles that still need to be dealt with.