Sickle cell disease (SCD) is a monogenic disease caused by mutations in the β-globin gene. The complications related to the disease are systemic as they impact multiple organ systems. Our goal in this study was to identify metabolome changes contributing to SCD-related severity. Employing both targeted and untargeted approaches, we profiled the plasma of 706 SCD patients using liquid chromatography tandem mass spectrometry. The cohort included 406 French patients of recent African descent and 300 African Americans from southeastern US. In total, we measured the levels of 61 known and 2,100 unknown metabolites. We applied weighted gene correlation network analysis (WGCNA) algorithms to account for correlations among metabolites and identify specific metabolomic modules associated with SCD-related complications. We constructed 66 modules containing at least 7 metabolites per module. Correlating these modules to 15 clinically important phenotypes (6 complications [e.g. stroke, acute chest syndrome] and 11 hematological traits [e.g. hemoglobin concentration, mean cell volume]), we found a module strongly associated with increased risks of gall bladder removal and another strongly associated with estimated glomerular filtration rate (eGFR). Using metabolomics, we identified metabolomic signatures of liver, gall bladder, and kidney complications in SCD.