This　paper　proposes　an　optimal　control　solution　for　regulating　the　dissolved　oxygen　(DO)　level　in　wastewater　treatment　processes　(WWTPs).　Our　method　integrates　the　Echo　State　Network　(ESN)　with　online　Adaptive　Dynamic　Programming　(ADP)　to　develop　an　ESN-ADP　controller　that　adeptly　handles　the　dynamic　and　nonlinear　properties　of　WWTPs.　Compared　to　the　traditional　Backpropagation　(BP)　neural　network,　which　may　not　be　optimal　for　handling　the　nonlinear　and　time-varying　dynamics　of　WWTPs,　ESN　provides　better　control　performance.　By　utilizing　an　online　learning　approach,　our　algorithm　guarantees　that　the　ESN-ADP　controller　is　both　adaptive　and　convergent　in　the　face　of　changing　conditions.　Results　of　experimental　tests　show　that　the　proposed　ESN-ADP　controller　outperforms　other　existing　control　methods　in　terms　of　regulatory　performance.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.