In　some　areas,　there　is　a　phenomenon　that　the　landfill　is　full　or　even　over-capacity　with　the　extension　of　the　service　period.　With　the　aging　and　damage　of　the　protective　facilities,　this　phenomenon　may　have　a　more　serious　impact　on　the　surrounding　environment.　It　is　necessary　to　excavate　and　transport　the　waste　beyond　the　part　to　control　it.　This　process　will　inevitably　produce　many　landfill　gas　emissions,　which　will　pollute　the　air.　Therefore,　it　is　necessary　to　predict　and　control　the　landfill　gas.　This　study　utilizes　the　Grey　Wolf　Optimization　(GWO)　algorithm　to　optimize　Support　Vector　Regression　(SVR).　It　establishes　prediction　models　for　various　LFG　concentrations　based　on　previous　LFG　concentration　data　and　real-time　environmental　monitoring　data.　The　models　are　compared　with　traditional　Support　Vector　Regression　and　Random　Forest　(RF)　algorithms,　predicting　the　concentrations　of　odor,　ammonia,　hydrogen　sulfide,　methane,　and　nitrogen　oxides.　The　results　indicate　that　GWO-SVR　demonstrates　more　stable　and　accurate　predictions　across　various　LFG,　with　the　coefficient　of　determination　R2　approximately　10%　higher　than　that　of　SVR　and　RF,　and　most　other　error　metrics　significantly　lower.　In　contrast,　SVR　and　RF　show　substantial　errors　in　predicting　odor,　hydrogen　sulfide,　and　nitrogen　oxides.　Thus,　the　GWO-SVR　algorithm　substantially　improves　the　performance　in　predicting　LFG　concentrations,　meeting　the　needs　of　on-site　management.