Background: Gliomas are highly aggressive brain tumors with nearly universal recurrence rate. Despite this, the ability to accurately predict tumor recurrence relies solely on serial MRI imaging, highlighting the need for prognostic biomarkers. Due to the low accuracies of individual serum markers, we have proposed the use of an integrated, multi-platform approach to biomarker discovery. Methods: A cohort of 107 glioma plasma samples, including 30 pairs, underwent plasma proteomic, consisting of a panel of serum proteins (FABP4, GFAP, NFL, Tau and MMP3,4 &7) quantified through ultrasensitive electrochemiluminescence multiplexed immunoassays, and plasma DNA methylation analysis, captured through cell-free methylated DNA immunoprecipitation and high-throughput sequencing. Results: Unsupervised hierarchal clustering revealed robust separation of primary and recurrent tumors through plasma proteomics, associated with a distinct plasma methylation signature. NFL, Tau and MMP3 levels differed between primary and recurrent samples; pair-wise analysis revealed increased in NFL and Tau concentrations upon recurrence. Tau levels predicted outcome independent of WHO Grade and IDH status. A predictive model created through the integration of the proteomic and methylation signatures revealed an AUC of 0.83. Conclusions: The combination of DNA methylation and plasma proteomics showcases that an integrative approach may improve the ability of these techniques for the serial monitoring of gliomas patients.