The complexity of contemporary language tasks has driven substantial research toward enhancing model architectures capable of processing intricate linguistic patterns with heightened efficiency and contextual precision. Introducing the Dynamic Synaptic Filtering Mechanism (DSFM), a novel approach designed to enable neural pathway modulation, represents a breakthrough in adaptable pathway activation within language models, allowing for dynamic adjustments to input complexity and task-specific demands. Implemented within a transformer-based large language model, DSFM facilitates selective synaptic activation by filtering pathways based on probabilistic assessments, thus offering a level of contextual sensitivity and computational efficiency previously unachievable with traditional static architectures. Comprehensive experiments reveal that DSFM reduces processing latency, optimizes memory allocation, and improves model accuracy, particularly in tasks requiring complex contextual understanding and syntactic variation. Quantitative results indicate that DSFM's probabilistic filtering substantially balances pathway utilization across layers, promoting both adaptive efficiency and computational stability under variable task loads. The findings suggest that DSFM enhances the structural and operational flexibility of language models, achieving a significant reduction in resource demands while maintaining linguistic accuracy, which positions DSFM as a transformative advancement in the pursuit of adaptable, resource-efficient language model architectures.