The remarkable evolution of machine learning architectures has greatly extended the capabilities of generative models, enabling more accurate, contextually-aware language generation across diverse applications. However, existing transformer-based language models encounter fundamental constraints when retaining contextual continuity over long interactions due to fixed attention windows and memory limitations, impeding coherence in extended dialogues. Addressing this challenge, Generative Memory Embedding (GME) introduces a novel memory augmentation framework that dynamically generates and retrieves memory embeddings aligned with ongoing discourse, ensuring relevance and contextual adaptation. This approach leverages adaptive memory mechanisms that synthesize and store contextual cues, enabling the model to maintain continuity by dynamically generating memory representations based on preceding interactions. Experimental results demonstrate substantial improvements in recall accuracy and response coherence, with the GME framework achieving marked gains over baseline models in long-range dependencies and response relevance. While the GME-enhanced model requires additional computational resources, the improvement in contextual integrity over extended dialogues provides a compelling basis for its integration into memory-augmented architectures. The demonstrated advantages of GME in memory efficiency and adaptive recall suggest significant potential for advancing next-generation language models capable of sustained, contextually-aware interaction.