Integrating Artificial Intelligence and IoT for Predictive Maintenance in Wastewater Systems

Wastewater infrastructure is critical to urban public health and environmental protection; however, aging systems and the high cost of reactive maintenance demand innovative predictive solutions. Recent research has demonstrated that integrating artificial intelligence (AI) with the Internet of Things (IoT) can enable predictive maintenance, optimize asset management, and reduce lifecycle costs. This literature review synthesizes findings from recent studies on sewer condition assessment models, including statistical approaches, artificial neural networks (ANN), and system dynamics, while also discussing recent advances in augmented reality (AR) for sewer visualization. The study reviews mathematical models ranging from linear regression and Markov chains to logistic regression for condition prediction, and we present a framework for integrating IoT sensor data with AI algorithms for real‐time monitoring and decision support. The review concludes with a discussion of challenges, limitations, and future research directions that promise to advance the state of the art in predictive maintenance for wastewater systems.