The evolution of complex language models has increasingly focused on enhancing the semantic coherence and contextual retention necessary for processing sophisticated linguistic structures over extended sequences. Traditional embedding techniques, while effective for straightforward applications, often fail to maintain fidelity across varying contexts and lengthy inputs, where semantic drift and interpretive degradation become pronounced. Introducing Semantic Depth Redistribution, a novel approach to embedding design, redefines contextual embedding preservation by redistributing semantic weight across hierarchical layers, enabling a balanced interpretative consistency within language models. Through this adaptive layering mechanism, the model achieves high degrees of coherence in multi-layer embeddings, preserving intricate semantic connections that traditional architectures tend to overlook. Extensive experimentation demonstrates that this redistributive framework not only enhances fidelity but also improves computational efficiency, showing considerable reductions in memory consumption and processing time without sacrificing output quality. The results demonstrate the practical potential of Semantic Depth Redistribution to address common challenges in contextual retention, offering a robust technique for improving large-scale language models in applications that demand high levels of interpretative accuracy and resource efficiency. The findings suggest promising implications for the continued refinement of embedding methodologies in computational language models.