The　real-time　detection　technique　and　comprehensive　characterization　of　dioxin　(DXN)　emission　concentration　during　the　municipal　solid　waste　incineration　process　persist　as　unresolved　challenges.　Prevailing　research　predominantly　relies　on　data-driven　models,　often　overlooking　the　potential　benefits　derived　from　fusing　combustion　mechanism　knowledge.　To　confront　this　issue,　we　propose　a　hybrid　modeling　strategy　that　fuses　a　simulator-based　mechanism　model　with　an　enhanced　regression　decision　tree-based　data　model.　This　approach　aims　to　predict　DXN　emission　concentrations　while　accommodating　diverse　time-scaled　measurement　requirements.　Based　on　virtual　mechanism　data　obtained　via　numerical　simulation　models　coupling　FLIC　and　Aspen　Plus,　we　constructed　a　white-box　surrogate　model　utilizing　a　multiple-input　multiple-output　linear　regression　decision　tree　(LRDT).　To　establish　a　relationship　with　DXN　emission　concentration,　we　employed　a　semisupervised　transfer　learning　mapping　model.　It　was　then　fused　with　a　novel　ensemble　LRDT　model　based　on　real　historical　data　by　using　a　constrained　incremental　random　weight　neural　network.　The　efficacy　of　this　modeling　strategy　was　validated　through　an　industrial　application　case　study　conducted　in　Beijing.