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ABSTRACT Background Ovarian cancer remains the most lethal gynecologic cancer and has the worst prognosis among all female reproductive malignancies worldwide. In Ethiopia, ovarian cancer is the third most prevalent malignancy among women, following breast and cervical cancers. Despite extensive research on the topic, evidence regarding the determinants of mortality among ovarian cancer patients remains limited. Therefore, the primary aim of this study was to identify predictors of ovarian cancer mortality among patients receiving care at oncology centers in Addis Ababa, Ethiopia. Methods A facility‐based case–control study was conducted among women with ovarian cancer enrolled at oncology centers in Addis Ababa. A total of 137 cases and 274 controls were selected using the stratified sampling method. Data were extracted from patient records with a structured data extraction tool. The effect of each predictor variable on ovarian cancer mortality was estimated using binary logistic regression at the 5% level of significance and the final model's goodness of fit was tested with the likelihood ratio test. Results This study included 411 ovarian cancer patients. Advanced FIGO stage (III&IV) (AOR: 2.85, 95% CI: [1.27–6.39]), advanced age (≥ 60) (AOR: 8.71; 95% CI: [3.89–19.48]), and comorbidity (AOR: 3.24, 95% CI: [1.69–6.19]) were found to be independent predictors of mortality, whereas urban residency (AOR: 0.36; 95% CI: [0.19–0.66]) and nonepithelial histological type (AOR: 0.12, 95% CI: [0.03–0.53]) were found to be protective factors. Moreover, patients not receiving pain medication had lower odds of mortality (AOR: 0.39; 95% CI: [0.22–0.69]). Conclusions Multivariable logistic regression analysis identified the following factors as significant predictors of mortality among ovarian cancer patients: advanced FIGO stage, comorbidities, advanced age, nonepithelial histology, pain medication, and urban residency. Therefore, early detection through timely clinical evaluation and diagnosis and treatment should be emphasized. Moreover, patients identified with significant predictors of mortality should receive targeted clinical attention and closer follow‐up to improve survival outcomes.