The　wastewater　treatment　is　an　important　avenue　of　resources　cyclic　utilization　when　coping　with　the　modern　urban　diseases.　However,　there　always　exist　obvious　nonlinearities　and　uncertainties　within　wastewater　treatment　systems,　such　that　it　is　difficult　to　accomplish　proper　optimization　objectives　toward　these　complex　unknown　platforms.　In　this　article,　a　data-driven　iterative　adaptive　critic　(IAC)　strategy　is　developed　to　address　the　nonlinear　optimal　control　problem.　The　iterative　algorithm　is　constructed　with　a　general　framework,　followed　by　convergence　analysis　and　neural　network　implementation.　Remarkably,　the　derived　IAC　control　policy　with　an　additional　steady　control　input　is　also　applied　to　a　typical　wastewater　treatment　plant,　rendering　that　the　dissolved　oxygen　concentration　and　the　nitrate　level　are　maintained　at　desired　setting　points.　When　compared　with　the　incremental　proportional-integral-derivative　method,　it　is　found　that　faster　response　and　less　oscillation　can　be　obtained　during　the　IAC　control　process.