Recent breakthroughs in deep learning architectures have allowed models to achieve unprecedented levels of accuracy and contextual understanding in generating humanlike text. However, significant challenges remain, particularly regarding inefficiencies in token processing, inference speed, and the occurrence of hallucinations during generation. Introducing a novel approach, Dynamic Token Clustering (DTC) redefines how tokens are processed by utilizing a context-sensitive clustering mechanism that dynamically groups semantically related tokens, optimizing computational resource allocation throughout the inference process. This technique not only accelerates inference but also improves model accuracy and reduces hallucination frequency by ensuring more focused attention on contextually significant tokens. Experimental results reveal substantial reductions in inference latency and memory usage, along with improvements in BLEU scores and perplexity across a range of language tasks. Moreover, DTC's ability to generalize effectively across different model sizes and tasks demonstrates its scalability and versatility. By addressing key inefficiencies and enhancing token relevance during inference, DTC establishes a new pathway for optimizing large-scale language models while maintaining high levels of output quality and reliability.