To　address　the　critical　challenges　in　furnace　temperature　(FT)　control　during　municipal　solid　waste　incineration　(MSWI),　such　as　control　uncertainties,　dynamic　time-varying　characteristics,　frequent　adjustments,　equipment　wear,　pollution　emissions,　and　high　energy　consumption,　we　propose　a　new　adaptive　FT　control　strategy　based　on　artificial　intelligence　(AI).　This　research　is　significant　as　it　aims　to　enhance　control　efficiency　and　adaptability　in　complex　operational　environments.　An　interval　type-2　fuzzy　broad　learning　system　(IT2FBLS)　controller　is　developed　based　on　an　analysis　of　FT　control　characteristics.　A　dynamic-static　switching　event-triggering　mechanism　(DSSETM)　is　designed　to　adaptively　switch　between　different　event-triggering　mechanisms,　thereby　reducing　the　update　frequency　of　the　manipulated　variable　(MV).　Additionally,　a　multi-type　eventtriggering　mechanism　(METM)　is　introduced　to　dynamically　update　both　the　controller　structures　and　control　laws,　further　enhancing　performance.　Stability　analysis　demonstrates　the　robustness　of　the　proposed　control　strategy　across　various　operational　stages.　The　proposed　AI　approach　is　implemented　through　experiments　using　real　data　from　municipal　solid　waste　incineration　(MSWI)　plants　and　a　hardware-in-the-loop　platform　(HILP).　This　work　contributes　to　the　application　of　AI　in　optimizing　FT　control　within　MSWI　processes,　aiming　to　effectively　address　uncertainties　in　the　FT　control　process　and　improve　control　performance　while　reduce　mechanical　wear　and　energy　loss,　demonstrating　both　its　effectiveness　and　practical　applicability.