The complexity and variation inherent in the tasks processed through language models increasingly demand that such systems not only retain semantic coherence but also adapt responsively to diverse contextual shifts across prompts. In response to this challenge, Neural Modulation for Dynamic Semantic Convergence (NMDSC) introduces an innovative neural architecture enhancement designed to enable on-the-fly adjustments to the language model's interpretative pathways. NMDSC accomplishes this through embedding modulation gates at strategic points within the model's intermediate layers, which recalibrate neural pathways dynamically, ensuring responses remain aligned with the semantic demands of changing prompts. Such modulation allows the model to maintain interpretative continuity and thematic relevance, particularly when handling complex or contextually complex inputs. Experimental implementation of NMDSC within an open-source language model showcased substantial advancements in response adaptability and coherence, with quantifiable improvements observed across various performance metrics and qualitative dimensions. Detailed assessments revealed that the NMDSC-enabled model exhibited enhanced semantic alignment and reduced error rates when responding to prompts with complex thematic requirements. The findings emphasize NMDSC's capacity to improve language model functionality, offering a scalable framework that enriches context-sensitive language generation, ultimately fostering more refined, consistent, and reliable interpretative outcomes across varied application scenarios. This approach to modular neural modulation thus positions NMDSC as a forward-looking solution to the adaptive challenges faced in current-generation language modeling tasks.