AI-Driven Decision Support Systems for Regional Infrastructure Optimization and Sustainable Development

Artificial intelligence-driven decision support systems are becoming central to how regions plan, prioritize, operate, and renew infrastructure under sustainability pressure. This review examines how AI, geospatial analytics, Internet of Things data, digital twins, optimization models, and explainable dashboards can improve regional infrastructure decisions across transport, water, energy, utilities, public facilities, and climate-resilience assets. The aim is to synthesize recent literature from 2020 to 2025 and propose an integrated review framework for AI-enabled regional infrastructure optimization. Following a structured review design inspired by systematic literature review conventions, the paper groups evidence into five themes: data integration, predictive intelligence, optimization and simulation, governance and ethics, and sustainable development outcomes. The review finds that AI decision support systems add value when they connect fragmented asset data with transparent scenario evaluation, risk forecasting, and multi-criteria prioritization. However, adoption remains limited by data silos, uneven regional capacity, model opacity, cybersecurity concerns, and weak links between technical analytics and public accountability. The paper contributes a practical framework that aligns AI-enabled decision support with regional planning objectives, Saudi Vision 2030 priorities, and global sustainable development goals. It concludes that AI should not replace professional judgement or community consultation; rather, it should create a disciplined evidence layer for faster, fairer, and more resilient infrastructure choices.