Abstract

As urban development accelerates globally, the demand for sustainable, inclusive, and accountable infrastructure has intensified. Environmental, Social, and Governance (ESG) metrics are increasingly central to evaluating the long-term impact and ethical footprint of urban projects. However, traditional ESG auditing methods often lack the granularity, scalability, and real-time responsiveness needed to manage the complexity of modern infrastructure ecosystems. This review explores the integration of Artificial Intelligence (AI) with ESG auditing frameworks in smart infrastructure, focusing on how AI technologies enable predictive sustainability assessment, automated compliance, and risk mitigation. The paper investigates the use of machine learning, natural language processing, and computer vision to collect, interpret, and report ESG-related data in urban development. It examines how AI can support green building certification, social equity mapping, and ethical governance audits by enhancing transparency and reducing reporting latency. Sectoral case studies illustrate the performance of AI-enabled ESG tools across transportation, housing, energy, and water infrastructure. The review concludes with a discussion of implementation challenges, policy recommendations, and future directions for AI-ESG synergy in shaping resilient, high-impact urban futures.