Large Language Models (LLMs) perform excellently on natural language generation tasks but often fail at tasks requiring rich contextual understanding, accurate reasoning, and the use of domain-knowledge. Such limitations can surface as hallucinations, drift in contexts, and classification errors for complex tasks involving summarization, question-answering, or classification. Contextual knowledge graphs are an opportunity because they enable the external enhancement of LLMs via dynamic, context-aware knowledge. This paper investigates the integration of dynamic knowledge graphs into LLMs to enhance their reasoning ability, contextual accuracy, and coherent output generation. It aims to ensure seamless transition into the LLM workflow by developing methods for task-specific, real-time retrieval of relevant data from KGs. The proposal of a contextual relevance filtering algorithm prevents information overload and prioritizes task-relevant knowledge. This study measures the impact of integrating KGs on a plethora of performance metrics such as accuracy, coherence, and context preservation, and majorly, complete evaluation on NLP tasks such as open domain question answering, document classification, and text summarization. In order to search for points of improvement and measure the level of improvement, this set of proposed models were compared against the conventional LLMs. Through the integration of KGs’ structured reasoning skills with LLMs’ dependability and contextual awareness, this study opens up to more reliable and domain-sensitive NLP applications by combining the ability of LLMs to create unstructured text with the organized reasoning skills of KGs.