The capacity of modern artificial intelligence systems to interpret and respond to intricate human language patterns has introduced significant advancements across diverse domains. However, maintaining contextual coherence over extended and semantically complex interactions remains an area of considerable challenge. The concept of Dynamic Semantic Bridging (DSB) introduces a highly novel approach designed to address this limitation through a recalibration mechanism that enhances contextual alignment within large language models (LLMs). By dynamically adapting to semantic shifts within evolving dialogues, DSB enables LLMs to retain thematic continuity, thereby advancing their ability to respond consistently to complex, multi-turn exchanges. The architectural design of DSB integrates a layered, dual-channel approach to contextual embedding, supporting selective prioritization of both primary and secondary context components within each interaction. Experiments reveal that DSB markedly improves coherence scores and contextual accuracy across varied datasets, with a reduction in semantic drift over long-form conversations, ultimately enhancing the interpretative precision of LLMs. Findings from this study highlight the transformative implications of DSB in applications where context-sensitive responses are essential, positioning DSB as a foundational advancement for improving the adaptability and coherence of LLMs in real-world deployments.