Grassland　gross　primary　productivity　(GPP)　is　an　important　part　of　global　terrestrial　carbon　flux,　and　its　accurate　simulation　and　future　prediction　play　an　important　role　in　understanding　the　ecosystem　carbon　cycle.　Machine　learning　has　potential　in　large-scale　GPP　prediction,　but　its　application　accuracy　and　impact　factors　still　need　further　research.　This　paper　takes　the　Mongolian　Plateau　as　the　research　area.　Six　machine　learning　methods　(multilayer　perception,　random　forest,　Adaboost,　gradient　boosting　decision　tree,　XGBoost,　LightGBM)　were　trained　using　remote　sensing　data　(MODIS　GPP)　and　14　impact　factor　data　and　carried　out　the　prediction　of　grassland　GPP.　Then,　using　flux　observation　data　(positions　of　flux　stations)　and　remote　sensing　data　(positions　of　non-flux　stations)　as　reference　data,　detailed　accuracy　evaluation　and　comprehensive　trade-offs　are　carried　out　on　the　results,　and　key　factors　affecting　prediction　performance　are　further　explored.　The　results　show　that:　(1)　The　prediction　results　of　the　six　methods　are　highly　consistent　with　the　change　tendency　of　the　reference　data,　demonstrating　the　applicability　of　machine　learning　in　GPP　prediction.　(2)　LightGBM　has　the　best　overall　performance,　with　small　absolute　error　(mean　absolute　error　less　than　1.3),　low　degree　of　deviation　(root　mean　square　error　less　than　3.2),　strong　model　reliability　(relative　percentage　difference　more　than　5.9),　and　a　high　degree　of　fit　with　reference　data　(regression　determination　coefficient　more　than　0.97),　and　the　prediction　results　are　closest　to　the　reference　data　(mean　bias　is　only　−0.034).　(3)　Enhanced　vegetation　index,　normalized　difference　vegetation　index,　precipitation,　land　use/land　cover,　maximum　air　temperature,　potential　evapotranspiration,　and　evapotranspiration　are　significantly　higher　than　other　factors　as　determining　factors,　and　the　total　contribution　ratio　to　the　prediction　accuracy　exceeds　95%.　They　are　the　main　factors　influencing　GPP　prediction.　This　study　can　provide　a　reference　for　the　application　of　machine　learning　in　GPP　prediction　and　also　support　the　research　of　large-scale　GPP　prediction.　©　2023　by　the　authors.