Wastewater treatment is the most important step in aqueous pollutant reduction and water environmental quality promotion. The treatment process stands up against many operating and regulating challenges. Influent variability makes the treatment process complicated, therefore, operators must monitor the biological, chemical, and physical water parameters, parallelly and continuously, and build appropriate operational controls on the systems. We propose to use novel Artificial Intelligence methods to improve the scientific understanding of physical-biological-chemical, highly complex, interactions during the process of wastewater treatment using unprecedented data size. Our proposal bundles two projects: a) Manage and understand Activated Sludge wastewater treatment plants operation and processes based on flocs and micro-organisms by AI image analyses, and b) Develop AI-based algorithms to understand, control, and manage the wastewater treatment process. The combination of the projects will allow us to build a novel data-driven management system that will provide excellent tools on how the wastewater should be operated, treated, monitored, and controlled.