In the revolution of the medical field, Biomedical Natural Language Processing (BioNLP) plays a major role in extracting meaningful entities from unstructured medical text. With the rise of advanced NLP technologies, medical entity extraction has become an essential task for understanding and organizing complex clinical language. The evolution of deep learning models, particularly transformer-based architectures, has enabled machines to interpret biomedical content with remarkable accuracy. Amid these advancements, the Hybrid BioBERT–BiLSTM model stands out as an intelligent and efficient framework for medical entity extraction — the process of identifying entities such as diseases, drugs, symptoms, and anatomical terms from clinical narratives. And a rule set is prepared to convert the unstructured medical data into a structured data. BioBERT, a domain-specific adaptation of BERT trained on biomedical literature, effectively captures the semantic meaning of medical terms, while BiLSTM enhances contextual understanding by processing information from both directions of a sentence. Using BIO-tagged datasets derived from medical transcription samples, this hybrid system accurately labels terms such as “asthma” as a Disease or “aspirin” as a Drug, thereby improving the automation and accuracy of medical data analysis. The model’s performance evaluation yielded a precision of 81.45%, recall of 82.23%, and an F1-score of 81.63%, demonstrating its robust capability in biomedical entity recognition