Predicting　the　permeate　flux　is　critical　for　evaluating　and　optimizing　the　performance　of　the　forward　osmosis　(FO)　process.　However,　the　solution　diffusion　models　have　poor　applicability　in　accessing　the　FO　process.　Recently,　the　data-driven　eXtreme　Gradient　Boosting　(XGBoost)　algorithm　has　been　proven　to　be　effective　in　processing　structure　data　in　engineering　problems　and　has　not　been　utilized　to　assess　the　FO　process.　Herein,　a　combination　of　the　XGBoost　model　with　a　genetic　algorithm　(GA)　was　first　proposed　to　predict　the　permeate　flux,　highlighting　its　superiority　in　the　FO　process　through　comparison　of　the　support　vector　regression　(SVR)　model,　the　artificial　neural　network　(ANN),　and　the　multiple　linear　regression　(MLR).　Moreover,　the　performance　of　these　models　was　optimized　by　tuning　hyperparameters　with　a　genetic　algorithm　(GA)　and　compared　via　Taylor　Diagram.　Among　these　machine　learning　(ML)　models,　the　GA-based　XGBoost　model　is　superior　to　the　other　three　models　in　terms　of　mean　square　error　(MSE,　2.7326)　and　coefficient　of　determination　(R2,　0.9721)　on　the　test　data,　and　its　prediction　power　was　compared　to　that　of　the　solution　diffusion　(SD)　model　in　the　literature.　Finally,　further　insight　into　the　feature　importance　that　affects　the　permeate　flux　in　the　FO　process　was　examined　by　utilizing　the　SHapley　Additive　exPlanations　(SHAP)　to　estimate　the　contribution　value　of　various　variables.　The　results　demonstrated　that　the　XGBoost　model　could　predict　the　permeate　flux　in　the　FO　system　with　high　accuracy　and　good　generalization　ability　for　the　given　data　set　and　even　on　the　unseen　data.　Furthermore,　the　findings　of　the　SHAP　method　show　that　the　osmotic　pressure　difference,　the　osmotic　pressure　difference　of　draw　solution　and　FS　solution,　the　crossflow　velocity　of　the　feed　solution　and　draw　solution,　and　the　water　permeability　coefficient　have　a　significant　impact　on　water　flux.