Background: Prostate cancer (PCa) ranks as the second most frequently diagnosed cancer and the sixth most prevalent cause of cancer-related mortality in men globally. Here, we employ a multiomic approach that utilizes both urine cfDNA and extracellular vesicle (EV) RNA-derived analytes to improve the risk stratification of individuals with PCa. Methods: Samples were collected from 106 individuals with elevated PSA levels (median: 6.5 ng/ml) who underwent urine collection before biopsy and MRI for comparative analysis. Within the cohort, 51 were diagnosed with high-grade prostate cancer (≥GG3), 11 were classified as intermediate risk (GG2), and 44 showed no or low-grade prostate cancer (≤GG1). EV-RNA and cfDNA were concurrently isolated from each urine specimen to exploit complementary data within the same sample. We developed an EV-RNA sequencing platform targeting mRNAs and lncRNAs and targeted 50 million reads per sample; cfDNA methylome profiling reached an equivalent sequencing depth. Expression of EV-RNA and splice variant Differential Transcript Usage (DTU) in addition to cfDNA methylation patterns were analyzed using Bio-Techne’s multiomic platform. Machine learning-based feature selection algorithms identified biomarker signatures from each analyte. Receiver-operator characteristic curves (ROC) were generated utilizing leave-one-out cross-validation of naïve Bayes classifier models to compute the area under the curve (AUC). Individual signatures were integrated to generate a multiomic classifier. Results: Differential gene expression (DEx) analysis identified DEx genes between low and high-risk patients with known and novel implications in PCa. Tissue deconvolution analysis revealed a high representation of testis, prostate, kidney, and lymphocyte in the EV-RNA samples. Additionally, splice variant analysis unveiled several genes previously implicated in PCa with DTU. Over 18,000 differentially methylated bases were detected between high and low-risk PCa patients and subsequent segmentation of the genome elucidated highly variable segments. Following feature selection analysis, AUCs obtained from top features of EV-RNA expression, splice variants, and cfDNA methylation, demonstrated an integrative multi-analyte signature with an AUC surpassing any individual analyte and MRI PI-RADS. While signatures obtained from each analyte resulted in the effective stratification of PCa risk, the multiomic signature further improved discriminatory power, highlighting the complementary nature of the signals. Thus, a multiomic strategy leveraging cfDNA and EV cargo exhibits significant potential as a next generation risk assessment tool for high-grade PCa. This approach has the capacity to facilitate more informed decision-making in disease management.