The recent surge in demand for contextually coherent and semantically accurate language model outputs demonstrates the persistent challenge of maintaining relevance across extended sequences, a task that traditional architectures struggle to accomplish effectively. Dynamic Semantic Drift Encoding (DSDE) introduces a novel framework designed to enhance contextual retention by dynamically adjusting semantic representations as text progresses, addressing limitations that often cause context degradation over long spans. Implementing DSDE within a state-of-the-art Large Language Model, this study demonstrates substantial improvements in coherence, relevance, and semantic alignment, achieving a level of consistency that surpasses baseline models. Experimental results reveal that DSDE not only effectively mitigates issues related to semantic drift and recency bias but also facilitates more natural long-range dependency modeling, proving beneficial for applications requiring a high degree of contextual accuracy. Moreover, findings indicate that DSDE contributes to a notable expansion in lexical diversity and adaptability across varied domains, confirming its versatility and practical significance in enhancing language model performance across diverse linguistic tasks. These results demonstrate DSDE's potential as an innovative and scalable approach to overcoming fundamental limitations in long-sequence contextual retention, marking a meaningful advancement in language model technology.