Recursive mechanisms have become essential in bridging the gap between linear processing capabilities and the intricate demands of extended textual coherence. Yet, traditional models often struggle to retain semantic and contextual clarity across evolving dialogues and multi-layered interactions, highlighting a pressing need for adaptive context handling frameworks. Introducing Recursive Context Layering (RCL), a novel approach that recursively structures context in Large Language Models (LLMs), this study provides a transformative methodology for retaining thematic alignment and managing complex discourse across extended text. Through recursive layering, RCL establishes a cumulative context system where each layer incrementally refines semantic depth, dynamically adapting to shifts in conversational focus while mitigating the challenges of semantic drift. The RCL framework allows LLMs to seamlessly adjust to varying levels of contextual granularity, maintaining response coherence in both structured and fluid language environments. Experimental findings substantiate the efficacy of RCL, demonstrating substantial improvements in semantic accuracy, contextual consistency, and retention metrics over baseline models, marking a significant leap in LLM design for tasks requiring sustained contextual depth and responsiveness. Overall, Recursive Context Layering not only enhances contextual fidelity in language processing applications but also sets a robust foundation for recursive strategies, positioning it as a crucial advancement for models handling complex and extensive discourse.