(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The increasing complexity and interdependency of critical infrastructure systems, from transportation and energy to water and telecommunications, demand a new generation of decision support tools that can integrate heterogeneous data streams, anticipate disruptions, and optimize operations under deep uncertainty. Artificial intelligence-enabled decision support systems (AI-DSS) offer transformative potential for smart infrastructure management by coupling real-time sensing, predictive analytics, and prescriptive optimization within layered socio-technical architectures. This paper presents a system-level examination of AI-DSS for infrastructure, moving beyond algorithmic novelty to address structural trade-offs, governance frameworks, robustness requirements, fairness constraints, sustainability imperatives, and deployment challenges. It delineates the architectural foundations that link edge sensing, digital twins, and multi-model inference pipelines, and it explores how these architectures shape accountability, transparency, and equity across public and private stakeholders. Detailed analytical discussion covers concept drift and adversarial resilience in safety-critical environments, the distributive justice implications of algorithmic resource allocation, life-cycle environmental costs of large-scale AI systems, and the institutional reconfigurations necessary for brownfield integration. Through cross-domain case illustrations spanning transport network control, water distribution management, and energy grid balancing, the paper reveals emergent tensions between efficiency, interpretability, and adaptive governance. It concludes by identifying forward-looking policy levers, including algorithmic auditing mandates, adaptive regulatory sandboxes, and multi-level participatory design processes that can align AI-DSS with long-term public values. The analysis underscores that the successful embedding of artificial intelligence into infrastructure decision-making is less a computational problem than a socio-institutional challenge requiring careful navigation of resilience, sustainability, and justice.