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Ovarian serous carcinoma (OSC) has high mortality, making accurate prognostic evaluation vital for treatment selection. This study develops a three-year OSC survival prediction model using machine learning, integrating pathological image features with clinical data. First, a Convolutional Neural Network (CNN) was used to classify the unlabeled pathological images and determine whether they are OSC. Then, we proposed a multi-scale CNN combined with transformer model to extract features directly. The pathological image features were selected by Elastic-Net and then combined with clinical information. Survival prediction is performed using Support Vector Machine (SVM), Random Forest (RF), and XGBoost through cross-validation. For comparison, we segmented the tumor area as the region of interest (ROI) by U-net and used the same methods for survival prediction. The results indicated that (1) the CNN-based cancer classification yielded satisfactory results; (2) in survival prediction, the RF model demonstrated the best performance, followed by SVC, and XGBoost was less effective; (3) the segmented tumor ROIs are more accurate than those predicted directly from the original pathology images; and (4) predictions combining pathological images with clinical information were superior to those solely based on pathological image features. This research provides a foundation for the diagnosis of OSC and individualized treatment, affirming that both ROI extraction and clinical information inclusion enhance the accuracy of predictions.