The growing demands on memory and contextual processing within expansive language models present critical challenges, particularly in resource efficiency and scalability across diverse linguistic tasks. Introducing a novel framework, Dynamic Contextual Scope Management (DCSM), this approach significantly redefines adaptive memory reallocation, equipping language models with a refined mechanism to allocate and segment memory based on dynamic context relevance. By realigning memory allocation in real-time to maintain only pertinent information, the DCSM framework achieves substantial improvements in memory usage, response coherence, and overall model adaptability. Experimental results demonstrate the framework's impact, with DCSM-enhanced models demonstrating up to 33.6% reductions in memory consumption and notable enhancements in response accuracy, latency, and computational efficiency. The integration of DCSM within large-scale language models not only addresses longstanding limitations in handling extended contexts but also establishes a scalable pathway for adaptive, high-performance language processing. These findings highlight DCSM's potential for transformative applications in contexts requiring efficient handling of extensive and evolving information, fostering a new standard for adaptive memory systems in the field of advanced language models.