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In this study, we explore the process of extracting metadata from academic documents using an automated approach. The objective is to establish a framework that enhances the accuracy and efficiency of metadata extraction from diverse document formats. We employed a combination of machine learning algorithms and natural language processing techniques to analyze the content and structure of documents. The methodology involved training models on a dataset of academic papers, allowing for the identification and extraction of key elements such as titles, authors, publication years, and relevant tags. Our results demonstrate that the implemented system achieves a high level of precision and recall, significantly improving the traditional manual extraction methods. The findings indicate that automating metadata extraction not only saves time but also enhances the accessibility of academic information for researchers and practitioners. Overall, this research contributes to the digital transformation of metadata handling in academic settings.