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The extraction of metadata from academic documents is a critical aspect of document management and retrieval. This study aims to develop an automated approach for accurately extracting essential metadata from diverse academic documents. Utilizing advanced text processing techniques and machine learning algorithms, the methodology focuses on identifying key elements such as title, authorship, publication year, and relevant tags. During the experimentation phase, a dataset comprising various academic papers was analyzed, leading to the creation of a robust testing framework. Results indicate that the proposed method achieved a high degree of accuracy in metadata extraction, successfully identifying and classifying more than 90% of the necessary information from the documents analyzed. This advancement not only streamlines the process of metadata extraction but enhances the efficiency of academic research workflows. The implications of this study are significant for scholars, libraries, and academic institutions looking to improve their document management systems. Future work will be directed towards refining the algorithms and expanding the database to include more document types, thus ensuring the methodology's applicability across a broader spectrum of academic literature.