In　the　past　few　decades,　global　industrial　development　and　population　growth　have　led　to　a　scarcity　of　water　resources,　making　sustainable　management　of　groundwater　a　global　challenge.　The　Water　Quality　Index　(WQI)　serves　as　a　comprehensive　method　for　assessing　water　quality　and　can　provide　valuable　recommendations　at　the　water　quality　level,　optimizing　policies　for　groundwater　management.　However,　the　subjectivity　and　uncertainty　of　the　traditional　WQI　have　negative　impacts　on　evaluation　outcomes,　particularly　in　determining　indicator　weights　and　selecting　aggregation　functions.　The　proposed　water　quality　index　for　groundwater　based　on　the　random　forest　(RFWQI)　model　in　this　study　addresses　these　issues.　It　selects　water　quality　indicators　based　on　the　actual　pollution　situation　in　the　study　area,　employs　an　advanced　random　forest　model　to　rank　water　quality　indicators,　determines　indicator　weights　using　the　rank　centroid　method,　scores　the　indicators　using　a　sub-index　function　designed　for　groundwater　development,　and　compares　the　results　of　two　commonly　used　aggregation　functions　to　identify　the　optimal　one.　Based　on　the　aggregated　scores,　the　water　quality　at　137　monitoring　sites　is　classified　into　five　levels:　＂Excellent＂,　＂Good＂,　＂Medium＂,　＂Poor＂,　or　＂Unacceptable＂.　Among　the　11　water　quality　indicators　(sodium,　sulfate,　chloride,　bicarbonate,　total　dissolved　solids,　fluoride,　boron,　nitrate,　pH,　CODMn,　and　hardness),　chloride　was　given　the　highest　weight　(0.236),　followed　by　total　dissolved　solids　(0.156),　and　sodium　was　given　the　lowest　weight　(0.008).　The　random　forest　model　exhibits　a　good　prediction　capability　before　hyperparameter　tuning　(86%　accuracy,　RMSE　of　0.378),　and　after　grid　search　and　five-fold　cross-validation,　the　optimal　hyperparameter　combination　is　determined,　further　improving　the　performance　of　the　random　forest　model　(94%　accuracy,　F1-Score　of　0.967,　AUC　of　0.91,　RMSE　of　0.232).　For　the　newly　developed　groundwater　sub-index　function,　interpolation　is　used　to　score　each　indicator,　and　after　comparing　two　aggregation　functions,　the　NSF　aggregation　function　is　selected　as　the　most　suitable　for　groundwater　assessment.　Overall,　most　of　the　groundwater　in　the　study　area　was　of　poor　quality　(52.5%　of　low　quality)　and　not　suitable　for　drinking.