The　burning　of　fossil　fuels　is　responsible　for　a　large　share　of　global　electricity　generation,　leading　to　the　emission　of　various　atmospheric　pollutants,　such　as　sulfur　dioxide　(SO2　).　Due　to　the　significant　release　of　SO2　from　coal　combustion,　wet　flue　gas　desulfurization　(WFGD)　technologies　are　widely　utilized　in　coal-powered　plants.　The　design　of　WFGD　modeling　systems　is　essential　for　enhancing　and　managing　the　desulfurization　process.　However,　WFGD　processes　in　industrial　settings　are　complex,　featuring　non-linear　behavior,　time　delays,　and　dynamic　uncertainties　driven　by　environmental　changes,　making　effective　dynamic　modeling　a　daunting　task.　This　study　presents　an　innovative　FGD　modeling　system　that　combines　machine　learning,　multi-model　approaches,　and　dynamic　neural　model　to　address　these　challenges.　The　system　achieves　high　accuracy　in　predicting　SO2　emission　concentration,　even　with　fluctuating　process　dynamics.　The　proposed　modeling　system＇s　effectiveness　and　practicality　are　validated　through　an　examination　of　a　real-world　WFGD　process.　Moreover,　its　flexible　structure,　real-time　capability,　and　exceptional　performance　highlight　its　broad　applicability　across　many　sectors.