Ovarian cancer (OC) is the most lethal gynecological malignancy, largely because non-specific symptoms contribute to advanced-stage diagnosis and poor prognosis, underscoring the need for improved non-invasive biomarkers. Circulating microRNAs (c-miRNAs) have emerged as promising candidates for OC detection, risk stratification, prognosis, and treatment monitoring. This systematic review, prospectively registered in PROSPERO (CRD420251230898), evaluated the diagnostic and prognostic performance of c-miRNAs and miRNA-based multivariate signatures. MEDLINE/PubMed, Scopus, and CENTRAL were searched from inception to 30 May 2026. Eligible studies assessed c-miRNAs in serum, plasma, whole blood, or extracellular vesicle fractions from women with confirmed or suspected OC including studies using prediagnostic samples from women subsequently diagnosed with OC. Records were screened, data extracted, and risk of bias evaluated according to the type of result using QUADAS-2 for diagnostic accuracy, QUIPS for prognostic-factor associations, and PROBAST for multivariable prediction models. Of 697 records identified, 91 publications met the inclusion criteria. Most studies used case–control or observational designs and analysed serum or plasma samples. Single c-miRNAs generally showed moderate diagnostic accuracy, whereas multivariate miRNA panels and multimodal models incorporating CA-125 or HE4 generally reported higher AUC values, including in some early-stage cohorts. Frequently reported candidates included members of the miR-200 family, miR-21, miR-1246, miR-125b, miR-145, miR-205, and miR-130a. Prognostic studies associated c-miRNAs with survival, recurrence, treatment response, and chemoresistance, although findings were more heterogeneous than diagnostic findings. Risk-of-bias concerns were common, particularly for patient selection in diagnostic studies, confounding in prognostic-factor analyses, and statistical analysis in prediction-model studies. Overall, c-miRNAs represent promising non-invasive biomarkers for OC, particularly when integrated into multivariate or multimodal models. However, methodological heterogeneity, small cohort sizes, limited external validation, and inconsistent reporting currently restrict clinical translation. Future studies should prioritize standardized pre-analytical and analytical workflows, adequately powered prospective cohorts, and independent multicenter validation.