Background:　Classification　of　infants　has　always　been　considered　a　crucial　task　in　the　literature　related　to　predicting　small　for　gestational　age　(SGA)　infants.　Traditional　medical　guidance　becomes　increasingly　unsatisfactory,　as　patients＇　care　should　be　centered　not　only　on　clinical　symptoms　but　also　on　socio-economic　and　demographic　factors.　Infants　with　excessive　gestational　weight　exhibit　serious　maternal　complications　that　require　early　intervention　to　streamline　the　progression　of　the　disease.　Methods:　This　research　proposes　to　use　the　Stacked　Generalization　Scheme　(SGS)　to　predict　Small　for　Gestational　(SGA)　Infants　on　the　dataset　collected　from　the　National　Pre-Pregnancy　and　Examination　Program　of　China.　A　Cleaned　Feature　Vector　(CFV)　is　created　that　entertains　issues　related　to　missing　values,　discretization　of　fields,　and　data　imbalance.　Later,　Knowledge-Driven　Data　(KDD)　Features　are　extracted　from　the　obtained　CFV,　and　the　proposed　scheme　is　applied　to　predict　SGA　infants.　The　proposed　scheme　superposed　the　existing　baseline　approaches　by　achieving　the　highest　precision,　recall,　and　AUC　scores　of　0.94,　0.85,　and　0.89,　respectively.　Conclusion:　The　proposed　SGS　can　predict　SGA　infants　accurately　compared　to　existing　baseline　schemes　using　KDD　parameters,　which　can　help　pediatricians　develop　an　efficient　SGA　Prognosis　process.