The expanding capabilities of language models have intensified the need for adaptive mechanisms that can manage dynamic contextual shifts in extended interactions. Current approaches primarily rely on static embeddings, which limit the model's responsiveness to real-time contextual variations, thereby hindering coherence in complex dialogues. The proposed Dynamic Language Interaction Mapping (DLIM) framework addresses this gap by introducing a novel methodology for continuous context-sensitive adaptation, enabling more fluid, relevant responses. Through a series of quantitative analyses, the DLIM-enhanced model exhibited substantial improvements across metrics such as contextual relevance, coherence over longer sequences, response diversity, and robustness to input noise, significantly outperforming baseline models. The architecture of DLIM not only advances the adaptability of language models but also broadens their functional applications, providing a scalable solution suitable for deployment in multiuser environments. These findings demonstrate the potential of DLIM to transform contextually adaptive language technologies, supporting a range of applications that require both high accuracy and flexibility in response generation.