The exponential increase in information volume handled by contemporary artificial intelligence systems has magnified the need for efficient, context-sensitive knowledge structuring within language models to support accurate and adaptable responses across diverse applications. Introducing Dynamic Cognitive Pathway Extraction, this study offers a novel solution that restructures retrieval pathways autonomously and dynamically, aligning with each query's contextual requirements. Designed to overcome the limitations of static and semi-static architectures, the pathway extraction method leverages a multi-layered approach to organize and prioritize knowledge nodes, achieving a high degree of retrieval coherence, reduced memory overhead, and optimized resource allocation under variable query loads. Through an experimental evaluation on an open-source language model, the results show that this adaptive pathway mechanism yields significant performance improvements, including reduced retrieval latency and enhanced memory utilization, especially in scenarios requiring rapid adaptability across complex and ambiguous queries. Furthermore, the pathway model's feedbackdriven refinement of retrieval structures enhances contextual retention, fostering a scalable, resource-efficient framework for knowledge management in AI-driven applications. Altogether, Dynamic Cognitive Pathway Extraction emerges as a robust methodological advancement, enabling intelligent systems to structure and retrieve knowledge with a precision and flexibility essential for the evolving demands of modern information processing.