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The Rapid Growth Of Digital Data Across Industries Has Created A Pressing Need For Efficient Methods Of Organizing And Extracting Meaningful Information. Document Classification And Data Extraction Have Emerged As Critical Techniques To Address This Challenge. Document Classification Leverages Machine Learning And Natural Language Processing (NLP) To Automatically Categorize Documents Based On Content, Structure, Or Intent, Enabling Streamlined Information Retrieval And Management. Complementarily, Data Extraction Focuses On Identifying And Retrieving Relevant Entities, Attributes, Or Patterns From Unstructured Or Semi-structured Documents, Transforming Raw Text Into Structured, Usable Datasets. Together, These Processes Enhance Decision-making, Improve Workflow Automation, And Reduce Manual Effort In Domains Such As Healthcare, Finance, Legal Systems, And Enterprise Operations. This Study Explores State-of-the-art Methodologies, Including Deep Learning Models, Rule-based Systems, And Hybrid Approaches, To Improve The Accuracy, Scalability, And Adaptability Of Document Classification And Data Extraction Systems. The Findings Highlight The Potential Of These Techniques To Unlock Hidden Insights, Reduce Information Overload, And Support Intelligent Information Systems In A Data-driven World.