Background: Thyroid dysfunction, including hypothyroidism (THO) and hyperthyroidism (THE), commonly arise from pathological processes in the thyroid gland. The current diagnosis of thyroid dysfunction varies because of the age and sex of the patients. The aim of this study was to explore novel candidate biomarker panels for hypothyroidism and hyperthyroidism screening with mass spectrometry and bioinformatics. Methods: Plasma samples were collected from 15 THE patients, 9 THO patients, and 15 healthy controls. Data Independent Acquisition(DIA)-based proteomic and untargeted metabolomic analyses were performed to identify novel biomarker panels for THO and THE patients. Finally, three candidate biomarkers were verified by ELISA in 34 samples. Results: A total of 2738 proteins and 6103 metabolites were identified, and 173 proteins and 2487 metabolites were found to be differentially expressed among the THE, THO and control groups. The results of the ensemble feature selection, K-means clustering and least absolute shrinkage and selection operator (LASSO) regression model showed that two proteins (C4-A and C3/C5 convertase) combined with two metabolites (L-arginine and L-proline), and proteins (APOL1 and ITIH4) combined with metabolites (cortisol, and cortisone) identified by plasma proteomics and metabolomics could help distinguish THO and THE patients from healthy controls, respectively. Conclusions: This study identified and verified two pairs of biomarker panels that can be used to distinguish THE and THO patients regardless of age and sex. Consequently, our findings represent a comprehensive analysis of thyroid dysfunction plasma, which is significant for clinical diagnosis.