Critical infrastructure systems increasingly depend on interconnected mechanical, electrical, and cyber–physical assets whose failures can generate substantial operational disruption, energy losses, and maintenance costs. Conventional preventive maintenance approaches, based primarily on fixed schedules and historical inspection, are insufficient for dynamically changing infrastructure conditions. This paper develops an intelligent hybrid Artificial Intelligence–Internet of Things (AI–IoT) model for sustainable infrastructure maintenance by integrating distributed sensing, edge–cloud computing, machine-learning-based anomaly detection, predictive failure assessment, and energy-performance monitoring. The proposed conceptual methodology combines vibration and operational sensing with hybrid analytical models to identify degradation patterns before critical failures occur. Edge processing is incorporated to reduce latency and unnecessary data transmission, while cloud-level analytics support longitudinal asset assessment and model refinement. Explainable AI is further integrated to improve maintenance decisions by making predictive outputs interpretable to infrastructure operators. The synthesized findings indicate that the strongest maintenance architecture is not based on a single predictive algorithm but on coordinated sensing, adaptive analytics, reliability evaluation, and energy-aware decision-making. The model consequently provides a framework for shifting infrastructure maintenance from reactive intervention toward condition-based and sustainability-oriented management. Its principal contribution is the integration of failure prediction and energy-loss reduction within a unified AI–IoT maintenance architecture.