In the past several decades, there has been an increase in the load on wastewater treatment (WWT) plants, largely due to rapid urbanization and industrial growth. The WWT plants still utilize “traditional” processes, which do not focus on energy recovery or preventive maintenance. Thus, there is a need for the treatment plants to move toward a new process management that is adaptive, data-driven, and intelligence controls powered by generative AI (GenAI). The GenAI models of this nature give an edge by allowing the simulation of complex biochemical processes, predicting the characteristics of the influent, and optimizing the treatment processes. Besides, the GenAI systems can work on the prediction of membrane fouling, aeration efficiencies, and sludge minimization, among others. Furthermore, GenAI plays a role in the creation of synthetic data, detection of anomalies, and regulatory compliance, thus providing an operational decision layer through which informed decisions can be made. Likewise, the convergence of digital twin systems supports the enhancement of smart plants’ capabilities in the continuous monitoring and predictive management of the plant, thus further easing the operations. The current chapter explores the WWT systems and how it can empower the modern GenAI models, such as GANs, VAEs, and transformer-based architectures for sustainability and intelligence through data quality, model interpretation, and the ethical aspect of automated decisions, particularly regarding critical infrastructure. The analyses of technical aspects and case studies suggest that GenAI connects the link between WWT and the realization of smart, adaptive, and resilient plants, which are the next generation of sustainability.