Biomedical papers are indispensable for Medical research and healthcare advancement, and these papers should, therefore, be indexed effectively. The analysis of complex expositions deciphering important biological text nuances in detail using a strong NLP numerical model such as RoBERTa increases the classification’s effectiveness. Context and Semantics puts RoBERTa in a position to categorize documents in a way that leads to retrieval and analysis of information. In a more specific manner, it has expert know-how of specific terminologies that make it much more dependable when it comes to the processing biological data. It helps to categorize and grouping much more effectively by capturing drug-nonprescription, drug-alcohol, drug-interaction, drug-target, drug-food & drug-drug. The organized technique also helps reveal the hidden pattern and potential medical breakthroughs discovered by academics and practitioners who seek and analyze texts. RoBERTa categorization is a universal categorization system for biomedical data to categorize and place it in an orderly manner. This to the extend that it will enhance the temperament of biological research, clinical decision making, as well as the general health of the global populace at a 93% accuracy.