With the rapid advances in generative AI, especially large language models (LLMs), the methodologies used in literature reviews have undergone significant transformation, enhancing both efficiency and quality. The chapter discusses how generative AI, particularly LLMs, strengthens the literature review process by refining key steps such as the formulation of research questions, the selection of relevant databases, and the development of effective search strategies. It highlights how LLMs support the extraction and synthesis of information, ensuring thorough analysis of diverse sources. Furthermore, the chapter considers the role of LLMs in organising and standardising data, leading to a more coherent and comprehensive synthesis of the literature. Concluding with an evaluation of current applications and future potential, the chapter presents practical case studies and step-by-step examples, offering students and researchers actionable insights on effectively leveraging AI to improve the depth, breadth, and quality of their literature reviews.