Large-scale language models have demonstrated exceptional capabilities across diverse applications, yet often struggle to dynamically incorporate real-time knowledge, which is essential for generating responses with high contextual relevance and coherence. Addressing this limitation, the concept of Dynamic Knowledge Interpolation (DKI) is introduced as a transformative mechanism enabling seamless integration of external knowledge sources directly into the generation process, enhancing adaptive responses across various domains. By facilitating continuous and context-sensitive interpolation between static and dynamic information, DKI significantly improves performance in key areas, including perplexity, contextual coherence, and response accuracy, as evidenced by quantitative analyses and case studies. The DKI architecture, rigorously tested across multiple datasets, shows that adaptability in language models can be markedly enhanced without sacrificing robustness or scalability, thereby establishing DKI as an advanced approach to refining language model efficacy. Through substantial performance gains and an ability to maintain contextual awareness in dynamically evolving scenarios, DKI positions itself as an essential framework in the advancement of responsive and domain-sensitive language models.