Municipal　solid　waste　incineration　(MSWI)　technology　plays　a　pivotal　role　in　addressing　solid　waste　disposal　challenges,　particularly　in　densely　populated　urban　areas.　This　paper　introduces　a　method　for　predicting　carbon　monoxide　(CO)　emissions　during　the　MSWI　process　utilizing　the　fast　Hoeffding　drift　detection　method　(FHDDM)　within　a　sliding　window　drift　detection　framework.　The　methodology　involves　the　development　of　a　long　short-term　memory　(LSTM)　neural　network　model　and　an　FHDDM　drift　index　calculation　model　based　on　historical　data　sets.　Each　online　sample　undergoes　recursive　standardization,　and　predictions　are　generated　using　the　historical　LSTM　model.　Subsequently,　the　prediction　errors　and　historical　drift　indicators　are　analyzed　to　identify　any　detected　drift.　If　no　drift　is　identified,　the　historical　model　is　employed　for　prediction　purposes.　However,　in　the　presence　of　drift,　the　LSTM　model　is　updated　by　integrating　both　historical　and　drift　data.　Real-time　assessments　and　updates　are　conducted　to　enhance　prediction　accuracy.　The　efficacy　of　this　approach　is　verified　through　actual　industrial　data　simulations　from　an　MSWI　facility　in　Beijing,　affirming　its　rationale　and　effectiveness.　©　2024　IEEE.