Dioxins　(DXN)　is　a　persistent　environmental　pollutant　that　poses　risks　such　as　a　weakened　immune　system,　and　teratogenic　and　carcinogenic　effects.　Municipal　solid　waste　incineration　(MSWI)　plants　are　one　of　the　major　DXN　generation　sources.　It　is　imperative　to　implement　the　monitoring　and　control.　However,　the　harsh　environment　prevents　the　use　of　conventional　equipment　for　detection,　resulting　in　a　lack　of　information　on　DXN　generation　concentration.　This　article　presents　an　advanced　tree-based　interpretable　deep　modeling　approach　that　utilizes　a　multimodal　data-driven　strategy.　The　available　data　types　include　two　modalities:　numerical　and　image　data.　To　address　the　above　issue　and　modeling,　first,　the　time　scale　of　the　multimodal　data　is　adjusted　to　match　the　sampling　period　of　DXN　based　on　the　mechanism　knowledge.　Then,　a　novel　adaptive　deep　forest　regression　algorithm　based　on　cross-layer　full　connection　(ADFR-clfc)　is　proposed　for　modeling　process　numerical　data　and　recorded　operational　data.　Furthermore,　a　convolutional　neural　network　feature　extraction　method　based　on　transfer　learning　combined　with　ADFR-clfc　is　employed　for　modeling　image　data.　Finally,　the　DXN　generation　concentration　is　obtained　by　taking　the　arithmetic　average　of　the　former　models.　The　proposed　method　is　validated　using　approximately　one　year　of　data　in　an　MSWI　plant　in　Beijing.　Experimental　results　show　that　the　root　mean　square　error　(RMSE)　of　the　concentration　estimate　is　0.0864　and　the　MAE　is　0.0707,　demonstrating　the　effectiveness　of　the　proposed　method.