The increasing demand for accurate and transparent Environmental, Social, and Governance (ESG) reporting, coupled with the rapid advancements in generative Artificial Intelligence (AI), presents a transformative opportunity for the ESG reporting landscape. Despite this potential, comprehensive research exploring the intersection of generative AI and ESG reporting remains limited. This systematic review aims to fill this knowledge gap by rigorously analyzing existing literature on the application of generative AI in ESG reporting. In accordance with the PRISMA guidelines, we systematically searched Scopus and IEEE Xplore databases, ultimately identifying nine relevant publications from an initial pool of 3679 articles. Our findings reveal that advanced generative AI models, such as GPT-4, and the integration of AI usage cards and human-AI collaboration frameworks, can significantly enhance the accuracy, efficiency, and transparency of ESG reporting. However, challenges related to data quality, bias, and the need for regulatory frameworks are also identified. This study promotes transparent and efficient ESG reporting practices by bridging the gap between research and practice. This review provides a comprehensive overview of the current state of generative AI in ESG reporting, highlighting its potential benefits and challenges. By synthesizing existing research, this review contributes to the scholarly understanding of this emerging field, offering valuable insights to guide future research and development. Moreover, it provides practical implications for practitioners seeking to leverage AI for ESG reporting and policymakers responsible for developing effective regulatory frameworks.