Uterine corpus endometrial carcinoma (UCEC) poses a significant to women's health. Accurate prediction of prognosis plays a crucial role in facilitating clinical decision-making processes. Therefore, this study aimed to develop a robust prognostic model based on gene expression profile. Gene expression profile of 546 UCEC samples of The Cancer Genome Atlas were retrieved. A multi-step strategy was employed to develop and validate a prognostic model predicting all-cause mortality rates. Receiver operating characteristic curve and decision curve analysis were performed to assess the predictive accuracy and net benefit of the model. Besides, model-associated immunological features were explored. The UCEC Prognostic Model (TUPM) performed well in identifying patients at high mortality risk. Patients with risk scores above the upper quartile had significantly decreased overall survival compared to patients with risk scores below the lower quartile (HR = 12.56, CI95: 4.629-34.09, P = 6.76E-7), indicating a prominent discriminability. The model accurately predicted patient survival from 1 to 5-year (area under the curve [AUC]1-year = 0.766, AUC2-year = 0.816, AUC3-year = 0.764, AUC4-year = 0.783, AUC5-year = 0.814) and provided excellent calibration. Meanwhile, The UCEC Prognostic Model encompassing transcriptome scores yielded a higher net clinical benefit than the baseline model that only included patient age and clinical stage. Furthermore, the prolonged survival in the low-risk group may be associated with increased infiltration of follicular T cells and regulatory T cells in the tumor microenvironment. We have developed a robust prognostic model for UCEC that may provide preliminary evidence for individualized management and treatment modality decision.