Recently, the number of women suffering from gestational diabetes mellitus (GDM) has risen dramatically. GDM attracts increasing attention due to its potential harm to the heath of both the fetus and the mother. We designed this case-control study to investigate the metabolome response of newborn meconium and urine to maternal GDM. GDM mothers (n = 142) and healthy controls (n = 197) were recruited during June-July 2012 in Xiamen, China. The newborns' metabolic profiles were acquired using liquid chromatography coupled to mass spectrometry. The data showed that meconium and urine metabolome patterns clearly discriminated GDM cases from controls. Fourteen meconium metabolic biomarkers and three urinary metabolic biomarkers were tentatively identified for GDM. Altered levels of various endogenous biomarkers revealed that GDM may induce disruptions in lipid metabolism, amino acid metabolism, and purine metabolism. An unbalanced lipid pattern is suspected to be a GDM-specific feature. Furthermore, the relationships between the potential biomarkers and GDM risk were evaluated by binary logistic regression and receiver operating characteristic analysis. A combined model of nine meconium biomarkers showed a great potential in diagnosing GDM-induced disorders.