Cuproptosis, a recently identified copper-dependent cell death mechanism, remains poorly unexplored in ovarian cancer (OC). This study systematically evaluates clinically significant cuproptosis-related genes (CRGs) as potential prognostic biomarkers in OC. Cox regression analysis and LASSO algorithms were used to develop a prognostic risk model incorporating 5 CRGs (CD8B2, GJB2, GRIP2, MELK, and PLA2G2D) within the TCGA cohort. This model stratified OC patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly shorter overall survival compared to the low-risk group. The model's predictive accuracy for prognosis in OC patients was validated in the TCGA training cohort, TCGA testing cohort, and ICGC external validation cohorts. Among these 5 signature genes, the number of cuproptosis genes associated with GJB2 is the largest, so we selected GJB2 for further validation. qPCR revealed that GJB2 was highly expressed in OC cells and tumor tissues. The high expression of GJB2 was closely associated with poor prognosis in OC patients. Functionally, GJB2 silencing suppressed OC cell proliferation and migration while its overexpression promoted malignant progression and EMT. Furthermore, GJB2 regulated copper homeostasis and reduced cuproptosis sensitivity, while also facilitating immune escape by inhibiting CD8 T cell infiltration and cytokine secretion, revealing its multiple roles in OC progression. In conclusion, we established a novel prognostic model incorporating 5 CRGs that effectively predicts clinical outcomes and characterizes the immune microenvironment in OC. Our findings particularly highlight GJB2 as a key regulator of cuproptosis with significant potential as both a prognostic biomarker and therapeutic target for OC management.