New challenges in designing scalable and adaptable neural architectures require innovative approaches to semantic representation and processing. The Semantic Entanglement Modeling (SEM) framework introduces a paradigm shift through its integration of modular semantic units, hierarchical attention mechanisms, and tensor-based operations, all configured to enhance coherence and task adaptability across diverse linguistic benchmarks. Its hybrid encoding scheme, combining static and contextual embeddings, demonstrates significant versatility in handling varied input complexities while ensuring resourceefficient operation. Experimental evaluations reveal substantial improvements in contextual alignment, with perplexity and entropy metrics highlighting the framework's robustness in managing high-dimensional data. Noise reduction through gating mechanisms enhances the precision of semantic outputs, particularly in tasks requiring minimal error tolerance. Fewshot learning experiments emphasize the model's adaptability, achieving high accuracy rates with limited task-specific data. Resource utilization analyses underline its scalability, showcasing consistent performance across multiple computational environments. The framework also addresses key inefficiencies in multi-modal integration, ensuring coherent semantic alignment across textual and non-textual inputs. Comparative analyses with existing architectures confirm that SEM not only reduces computational overhead but also extends applicability to specialized and high-complexity tasks. Its contributions are further evidenced in the context of generalization capabilities, where robust performance was observed even under variable input distributions. The SEM framework exemplifies a transformative approach to optimizing neural models for diverse linguistic challenges, delivering significant advancements in performance, efficiency, and scalability.