This　paper　takes　a　portion　of　the　Manas　River　Basin　in　Xinjiang　Province,　China,　as　an　example　and　proposes　an　improved　traditional　comprehensive　water　quality　index　(WQI)　method　using　Extreme　Gradient　Boosting　(XG-BOOST)　to　analyze　the　groundwater　quality　levels　in　the　region.　Additionally,　XG-BOOST　is　used　to　screen　the　existing　dataset　of　ten　water　quality　indicators,　including　fluoride　(F),　chlorine　(Cl),　nitrate　(NO),　sulfate　(SO),　silver　(Ag),　aluminum　(Al),　iron　(Fe),　lead　(Pb),　selenium　(Se),　and　zinc　(Zn),　from　246　monitoring　points,　in　order　to　find　the　dataset　that　optimizes　model　training　performance.　The　results　show　that,　in　the　selected　study　area,　water　quality　categorized　as　＂GOOD＂　and　＂POOR＂　accounts　for　the　majority,　with　＂GOOD＂　covering　48.7%　of　the　area　and　＂POOR＂　covering　31.6%.　Regions　with　water　quality　classified　as　＂UNFIT＂　are　mainly　distributed　in　the　central-eastern　parts　of　the　study　area,　located　in　parts　of　the　Changji　Hui　Autonomous　Prefecture.　Comparatively,　water　quality　in　the　western　part　of　the　study　area　is　better　than　that　in　the　eastern　part,　while　areas　with　＂EXCELLENT＂　water　quality　are　primarily　distributed　in　the　southern　parts　of　the　study　area.　The　optimal　water　quality　indicator　dataset　consists　of　five　indicators:　Cl,　NO,　Pb,　Se,　and　Zn,　achieving　an　accuracy　of　98%,　RMSE　=　0.1414,　and　R2　=　0.9081.