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Water treatment systems have been implemented by urbanizing societies for millennia to facilitate water management goals. Common models of surface overflow rate (SOR), plug flow reactor (PFR), and continuously stirred-tank reactor (CSTR) were developed through conceptual, empirical, and analytical tools; implemented based on idealized hydrodynamics and geometrics. More recently, computational fluid dynamics (CFD) and artificial intelligence (AI), from evolutionary optimization to machine learning (ML) methods, have been introduced. AI methods can be effectively coupled with CFD simulations to optimize water treatment. In this study, CFD coupled with physical models and selected ML and optimization tools, including DeepXtorm, are examined with respect to design, treatment analysis, and retrofits, providing significant economic and treatment benefits.