This　research　proposes　to　use　ensemble　learning　methods　to　diagnose　and　predict　Turner　syndrome　using　facial　images.　Turner　syndrome,　also　known　as　congenital　ovarian　hypoplasia　syndrome,　is　a　common　clinical　chromosomal　disorder.　Without　the　aid　of　cytogenetic　diagnostic　results,　the　accuracy　of　diagnosis　made　by　the　paediatrician　is　unsatisfactory.　Early　diagnosis　of　the　Turner　syndrome　requires　the　expertise　of　well-trained　medical　professionals,　which　may　hinder　early　intervention　due　to　a　high　potential　cost.　So　far,　most　of　the　studies　have　reported　the　use　of　clinical　chromosome　detection　to　diagnose　Turner　syndrome.　In　this　research,　we　are　the　first　to　use　facial　recognition　technology　to　diagnose　Turner　syndrome　using　ensemble　learning　techniques.　First,　the　features　from　each　of　the　facial　image　are　extracted　by　principal　component　analysis,　kernel-based　principal　component　analysis,　and　others.　Second,　we　randomly　selected　samples　and　features　to　establish　a　basic　learning　model.　Finally,　we　developed　a　combination　of　multiple　basic　learning　models　using　majority　voting　and　stacking　for　the　facial　image　classification　task.　Experimental　results　show　that　the　correct　classification　rate　of　the　Turner　syndrome　detection　was　elevated　up　to　88.1%.　The　proposed　method　can　be　implemented　to　automatically　diagnosis　Turner　syndrome　patients　that　can　facilitate　clinicians　during　the　prognosis　process.