Efficiently　predicting　effluent　quality　through　data-driven　analysis　presents　a　significant　advancement　for　consistent　wastewater　treatment　operations.　In　this　study,　we　aimed　to　develop　an　integrated　method　for　predicting　effluent　COD　and　NH3　levels.　We　employed　a　200　L　pilot-scale　sequencing　batch　reactor　(SBR)　to　gather　multimodal　data　from　urban　sewage　over　40　d.　Then　we　collected　data　on　critical　parameters　like　COD,　DO,　pH,　NH3,　EC,　ORP,　SS,　and　water　temperature,　alongside　wastewater　surface　images,　resulting　in　a　data　set　of　approximately　40246　points.　Then　we　proposed　a　brain-inspired　image　and　temporal　fusion　model　integrated　with　a　CNN-LSTM　network　(BITF-CL)　using　this　data.　This　innovative　model　synergized　sewage　imagery　with　water　quality　data,　enhancing　prediction　accuracy.　As　a　result,　the　BITF-CL　model　reduced　prediction　error　by　over　23%　compared　to　traditional　methods　and　still　performed　comparably　to　conventional　techniques　even　without　using　DO　and　SS　sensor　data.　Consequently,　this　research　presents　a　cost-effective　and　precise　prediction　system　for　sewage　treatment,　demonstrating　the　potential　of　brain-inspired　models.[Figure　not　available:　see　fulltext.].　©　2024,　The　Author(s).