This paper presents a review of the most recent literature (2024–2025) on the application of Artificial Intelligence (AI) in ESG reporting, focusing on benefits, challenges, and risks for companies and decision-makers. Both academic and professional sources were analyzed, including international and regional examples, addressing automated data collection and verification, sustainability indicator modeling, and real-time performance monitoring. Findings indicate that AI improves the accuracy, coverage, and timeliness of ESG information, reduces compliance costs, and facilitates regulatory alignment. At the same time, key risks were identified: model opacity, bias and inconsistent input data quality, the risk of AI-driven greenwashing, privacy concerns, and fragmented standards. The paper synthesizes best practices for responsible AI use (data governance, auditability, model documentation, and bias control) and proposes a framework for evaluating the maturity of AI solutions in ESG reporting. Regional challenges, particularly in the Balkans, where ESG standard implementation still varies, are also discussed. In conclusion, AI can significantly enhance the credibility and usability of ESG metrics, but only with robust controls, transparency, and clear regulatory compliance, highlighting avenues for further research and practical improvement.