The development of increasingly sophisticated language models has led to substantial progress in generating coherent and contextually relevant text for a wide range of applications. Despite these advancements, challenges persist in ensuring that models can integrate newly acquired knowledge without undergoing resource-intensive retraining, which severely limits their adaptability and scalability in dynamic environments. Dynamic Knowledge Synchronization (DKS) emerges as a novel framework addressing this limitation through the introduction of selective, modular updates that allow models to retain their core knowledge while incrementally adapting to new information. By compartmentalizing the knowledge architecture and integrating reinforcement-based feedback mechanisms, DKS offers an efficient method to incorporate new data without compromising the stability and coherence of previously learned knowledge. The study presented here demonstrates the efficacy of DKS in overcoming the rigidity of traditional large-scale training processes through modular parameter adjustments and selective synchronization procedures. Experimental results showed significant improvements in model adaptability, knowledge retention, computational efficiency, and response consistency when compared to conventional fine-tuning or retraining methods. The novel architecture introduced through DKS not only provides a scalable approach to continual learning but also enhances the model's ability to operate effectively across diverse and evolving domains, thereby extending its applicability to real-world scenarios requiring frequent and incremental knowledge updates. Furthermore, the incorporation of attention-based filters and targeted parameter adjustments in DKS contributes to reducing computational overhead, enabling more efficient deployment in resource-constrained environments. This innovative approach redefines the framework for updating and managing knowledge within language models, offering a more sustainable solution for long-term learning while maintaining high responsiveness and consistency in performance.