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Systemic autoimmune diseases (SADs) are characterized by dysfunctioning of the immune system, which causes damage in several tissues and organs. Among these pathologies are systemic lupus erythematosus (SLE), systemic sclerosis or scleroderma, Sjögren’s syndrome, rheumatoid arthritis, primary antiphospholipid syndrome (PAPS), mixed connective tissue disease (MCTD), and undifferentiated connective tissue disease (UCTD). Early diagnosis is difficult due to similarity in symptoms, signs, and clinical test results. Hence, our aim was to search for differentiating metabolites of these diseases in plasma and urine samples. We performed metabolomic profiling by liquid chromatography–mass spectrometry (LC–MS) of samples from 228 SADs patients and 55 healthy volunteers. Multivariate PLS models were applied to investigate classification accuracies and identify metabolites differentiating SADs and healthy controls. Furthermore, we specifically investigated UCTD against the other SADs. PLS models were able to classify most SADs vs healthy controls (area under the roc curve (AUC) > 0.7), with the exception of MCTD and PAPS. Differentiating metabolites consisted predominantly of unsaturated fatty acids, acylglycines, acylcarnitines, and amino acids. In accordance with the difficulties in defining UCTD, the UCTD metabolome did not differentiate well from the other SADs. However, most UCTD cases were classified as SLE, suggesting that metabolomics may provide a tool to reassess UCTD diagnosis into other conditions for more well-informed therapeutic strategies.