Semantic Morphogenesis introduces a transformative framework designed to address limitations in contextual adaptability and semantic coherence within language models. Through the integration of tensorial dynamics, the approach enhances the capacity to process high-dimensional semantic relationships across varied linguistic contexts. The study implements a novel tensorial framework that dynamically refines attention mechanisms, enabling improved alignment between modelgenerated outputs and complex input semantics. Quantitative analyses demonstrate significant improvements in contextual accuracy, coherence, and robustness to input noise, with tasks spanning semantic similarity, ambiguity resolution, and summarization. The integration of low-rank tensor approximations facilitates computational efficiency, ensuring scalability across diverse domains and resource constraints. Performance comparisons reveal that the proposed model surpasses traditional transformer-based architectures, particularly in generalization to domain-specific tasks and few-shot learning scenarios. Statistical validations confirm the reliability and significance of the enhancements, highlighting the robustness of the methodology. Results further underscore the utility of tensorial embeddings in capturing nested syntactic structures and multi-scale dependencies, contributing to a more comprehensive understanding of contextual semantics. The proposed framework successfully bridges critical gaps in existing models, advancing capabilities in domain adaptation and semantic precision. Experimental results validate the adaptability and resilience of the approach across various contextual complexities, demonstrating its potential for widespread applicability. By leveraging tensorial dynamics, the study establishes a foundation for advancing computational semantics and enabling more sophisticated linguistic modeling. The outcomes suggest a promising direction for future innovations in the design and optimization of language models.