Background: Despite ongoing debate about reliable prognostic factors, this study aimed to develop and validate a novel nomogram for predicting cancer-specific survival (CSS) in patients with ependymoma. Methods: Clinical data from the Surveillance, Epidemiology, and End Results database spanning 2000 to 2018 were used for the analysis. Data were randomly categorized into development and validation groups in a 7:3 ratio. Univariate and multivariate Cox regression analyses and LASSO regression were conducted to identify independent risk factors for CSS. Predictive models were evaluated using calibration plots,  concordance index (C-index),  and area under the receiver operating characteristic curve (AUC). Additionally, decision curve analysis and clinical impact curves were conducted. Results: The final sample comprised 2,340 patients. Multivariate analysis identified race, age, histological type, surgery, and tumor site as independent predictors of CSS. A nomogram incorporating these risk factors was developed to predict CSS  in patients with ependymomas. Calibration plots demonstrated a high level of consistency between predicted and actual values. The C-index for the training cohort was 0.746 (95% CI: 0.715–0.777), and for the validation cohort, it was 0.743 (95% CI: 0.698–0.788). Additionally, both AUC and decision curve analysis analyses indicated robust performance and clinical significance benefits. Kaplan–Meier curves further demonstrated the nomogram’s strong ability to predict patient outcomes. Conclusions: Our nomogram has the potential to offer significant value in predicting the outcomes of patients with ependymomas aged > 1, 5, and 8 years. This predictive model will assist doctors and patients in devising effective clinical strategies.