This study aims to explore a new approach to reduce the recurrence risk and improve the prognosis of ovarian cancer (OC) patients with abdominal metastasis by analyzing the clinical characteristics and prognostic factors. A total of 292 OC patients with abdominal metastasis, treated at Henan Provincial People's Hospital between 2021 and 2023 were included in this retrospective study. Follow-up was conducted for one year to observe the recurrence, with 285 patients completing the observation. The patients were then categorized into relapsing and non-relapsing groups based on whether they experienced a relapse within one-year follow-up. Independent sample t-tests and χ 2 tests were used for inter-group comparison. Both univariate and multivariate logistic regression analyses were utilized to screen factors affecting recurrence. The variance inflation factor (VIF) was used to analyze whether the variables in the model had multicollinearity. Receiver Operating Characteristic (ROC) curves and nomographs were used to construct models for predicting one-year recurrence in OC patients with abdominal metastasis. Area under curve (AUC) of ROC and Hosmer-Lemeshow goodness of fit test were used to evaluate the accuracy of the model. The prediction model was verified by internal verification and external verification. The number of pregnancies, the number of births, diabetes mellitus, tumor diameter, tumor reduction combined with intraperitoneal chemotherapy, CA-125, HE-4, NLR, PLR, MLR showed association with patient recurrence. Logistic regression analysis revealed that lower pregnancy frequency and elevated levels of CA-125, HE-4, PLR and MLR were independent risk factors for increased risk of recurrence. In addition, the nomogram-based model demonstrated strong predictive accuracy for one-year recurrence. OC patients with abdominal metastasis present diverse clinical manifestations, among which fewer pregnancies and elevated levels of CA-125, HE-4, PLR, and MLR may be independent risk factors for increased risk of recurrence. Individualized interventions based on these prognostic factors are essential to reduce risk and enhance patient quality of life.