Contextual reasoning in language models has historically faced challenges in maintaining coherence over extended sequences and adapting to evolving contextual requirements. Context-Dependent Memory Synthesis (CDMS) offers a novel mechanism that integrates dynamic memory pathways into transformer-based architectures, enabling improved retention, retrieval, and synthesis of contextual information. The framework incorporates multi-layered memory modules and adaptive attention mechanisms, allowing language models to dynamically encode and retrieve contextually relevant information across diverse linguistic tasks. Experimental evaluations demonstrated significant reductions in perplexity and enhanced memory utilization, highlighting the efficiency of CDMS in handling longterm dependencies. Robustness against noisy inputs was achieved through dynamic memory updates, ensuring coherence even in challenging scenarios with imperfect data. Scalability assessments revealed consistent performance across varying model sizes, with notable improvements in latency and throughput metrics. The generalization capabilities of the architecture were validated through tasks spanning scientific, legal, and conversational domains, achieving high accuracy and retention rates without extensive fine-tuning. Computational benchmarks confirmed the feasibility of integrating CDMS into existing transformer frameworks, offering a balance between resource efficiency and taskspecific adaptability. Quantitative results also demonstrated low variability in latency across diverse tasks, affirming the system's stability and responsiveness. The findings demonstrate the transformative potential of CDMS in addressing key limitations in traditional language model architectures while providing a robust foundation for future innovations in neural memory systems.