Knowledge Graph Question Answering (KGQA) is an evolving field that aims to leverage structured knowledge graphs to provide precise answers to user queries. As Knowledge Graphs continue to expand in complexity and size, efficiently navigating and extracting relevant information from these vast datasets has become increasingly challenging. Recent advancements in Large Language Models (LLMs), offer promising capabilities in understanding and processing natural language. By integrating LLMs with KGQA systems, it is possible to enhance the accuracy and contextual relevance of answers generated. In this chapter, we explore the intersection of KGQA and LLMs, evaluating their combined potential to fetch information from knowledge graphs.