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The Increasing Volume Of Digital And Textual Evidence In Forensic Investigations Necessitates Advanced Techniques For Efficient Information Extraction And Analysis. This Study Explores The Application Of Natural Language Processing (NLP) Methods, Particularly Transformer-based Models, To Automatically Extract Relevant Entities, Relationships, And Events From Forensic Documents. Leveraging The Contextual Understanding Of Transformers, The System Identifies Key Forensic Information With High Accuracy And Reduces Manual Effort In Case Analysis. Extracted Data Is Further Structured Into Knowledge Graphs, Enabling Intuitive Visualization Of Complex Relationships Between Entities Such As Suspects, Locations, Incidents, And Evidence. This Approach Not Only Enhances The Speed And Precision Of Forensic Investigations But Also Facilitates Pattern Recognition, Trend Analysis, And Decision-making. The Integration Of Transformer-based NLP With Graph Visualization Represents A Promising Paradigm For Modernizing Forensic Intelligence And Improving Investigative Outcomes.