Biomedical image segmentation plays a pivotal role in diagnostic radiology and computational pathology, enabling precise delineation of anatomical and pathological structures. However, despite advancements in deep learning-based segmentation, challenges persist in interpretability, computational tractability, and scalability. This paper proposes an advanced computational framework that integrates Large Language Models (LLMs) with segmentation architectures, quantum databases for accelerated query performance, and optimized image compression techniques. The proposed system leverages mathematical principles of variational optimization, tensor decomposition, and quantum search complexity to enhance segmentation efficiency, reduce latency, and improve decision support. A rigorous comparative analysis is performed using benchmark datasets, demonstrating superior segmentation accuracy, reduced query response time, and improved data storage efficiency. The integration of LLMs provides an interpretable interface for clinicians and radiologists, enhancing the usability of automated segmentation in real-world medical workflows.