Artificial intelligence for sustainable and resilient infrastructure systems: a review of innovations across energy, water, transportation, and urban development

Artificial intelligence (AI) is increasingly applied to infrastructure systems to improve efficiency, resilience, sustainability, and decision-making in rapidly changing, uncertain environments. This study presents a systematic literature review and analytical synthesis of AI applications in sustainable and resilient infrastructure systems, focusing on energy, water, transportation, and urban development. The review examined peer-reviewed studies and selected institutional reports identified primarily through Google Scholar, SpringerLink, ScienceDirect, and relevant international organizations, with emphasis on recent literature published between 2020 and 2026. The study screened retrieved studies and thematically categorized them by infrastructure sector, data sources and types, data-collection technologies, data infrastructure and platforms, AI methods, decision-support functions, sustainability and resilience outcomes, and implementation challenges. The synthesis shows that AI supports predictive monitoring, demand forecasting, anomaly detection, resource optimization, infrastructure management, and decision support across the four sectors. However, the evidence remains uneven across sectors and geographic contexts, with substantial gaps in long-term empirical validation, cross-sector integration, interoperable data ecosystems, lifecycle assessment, and applications in developing regions. The review also identifies challenges related to data quality and availability, digital infrastructure, interoperability, cybersecurity, institutional capacity, digital inequality, and the resource requirements associated with maintaining AI-enabled infrastructure systems. This review’s novel contribution is an integrated data–AI infrastructure–sustainability perspective that connects data sources and collection technologies with data infrastructure, AI methods, decision-support functions, and infrastructure outcomes. This perspective provides a basis for understanding how AI can move beyond isolated, sector-specific applications toward more integrated, adaptive, resilient, and innovative AI-driven solutions for sustainable infrastructure systems.