The increasing computational demands and inefficiencies associated with the scaling of advanced language models present significant challenges, particularly in terms of maintaining accuracy, inference speed, and resource utilization. Addressing these issues, the introduction of Dynamic Token Compression (DTC) and Adaptive Layer Pruning (ALP) offers a novel and transformative approach to optimizing model performance, significantly reducing token redundancy and selectively pruning model layers based on the complexity of input queries. These methods enable enhanced language models to generate more coherent, contextually accurate outputs while minimizing computational load, making them more suitable for real-time applications. Through comprehensive experimentation, DTC and ALP were shown to improve model efficiency across multiple key metrics, including perplexity, BLEU score, and hallucination rate, while also reducing memory usage and energy consumption. The ability to dynamically adjust token processing and layer utilization without compromising on performance demonstrates the practical value of these techniques for large-scale model deployments in various domains. By optimizing both computational and linguistic aspects of the models, this research provides a robust framework for future advancements in language model architecture.