AI-enabled digital twins are moving from simulation-oriented representations toward operational decision systems for critical infrastructure, but deployment evidence remains uneven and highly sector dependent. This review synthesizes AI-enabled digital twins in water and power infrastructure using a structured narrative evidence base of 36 primary application studies, balanced between 18 water and 18 power studies, together with recent reviews, interdependency research, and governance standards. The review compares application domains, data environments, AI roles, synchronization demands, validation settings, and decision authority across both sectors. Three analytical contributions extend beyond application cataloguing: a six-level deployment-maturity model with observable criteria; a coupled water–power framework for physical, cyber/data, spatial, and organizational cascading risk; and a trustworthy-AI pathway linking explainability, uncertainty, human oversight, fallback, traceability, interoperability, and re-validation. The evidence shows that water twins are strongly shaped by heterogeneous sensing, hydraulic/process uncertainty, and site-specific transfer, whereas power twins more often face tight latency, topology change, stability margins, and protection-adjacent decision requirements. Across both sectors, the dominant unresolved gap is not isolated AI accuracy but sustained field evidence demonstrating synchronized physical linkage, uncertainty-aware decisions, governed authority, cyber resilience, and lifecycle re-validation. The review therefore provides a cross-sector basis for distinguishing promising AI models from deployment-ready digital twins and for prioritizing future infrastructure-grade validation.
link
https://doi.org/10.54216/MOR.070205