Bladder cancer (BC) is a common malignancy associated with high morbidity and mortality, however, accurate and convenient risk assessment tools applicable to BC patients are currently lacking. Previous studies using nomograms to evaluate bladder cancer (BC) survival have been based on small samples. Using a large dataset, this study aimed to construct more precise clinical nomograms to effectively predict bladder cancer survival.Data on patients with pathologically-confirmed bladder cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Additional BC patient data for an external validation cohort were extracted from the Cancer Genome Atlas (TCGA) database. Clinical parameters that constituted potential risk factors were reviewed and analyzed using univariate and multivariate Cox proportional hazards regression. A nomogram was constructed with parameters that significantly correlated with the overall survival (OS). Prognostic performance of a nomogram was assessed using the concordance index (c-index), area under the receiver operating characteristic curve (AUC), and a calibration curve. The model was then tested with data from an internal and external validation cohort. Patients' survival was analyzed and compared with the Kaplan-Meier (KM) method.Multivariate Cox regression showed that age, sex, race, stage_T1, stage_T2a, stage_T2b, stage_T3a, stage_Ta, stage_Tis, stage_N, stage_M were independent predictors of BC survival. A nomogram was constructed based on these factors. The c-index of the nomogram was 0.7916 (95% confidence interval CI, 0.79-0.80). The calibration curve showed excellent agreement between the predicted and observed values. The c-index for the internal validation cohort was 0.7917 (95% CI 0.79-0.80), which was higher than for the training cohort, suggesting robustness of the model. For the training cohort, the AUC for the 3- and the 5-year survival was 0.82 and 0.813, respectively. The c-index for the TNM-based model was superior to that for the AJCC-TNM classification.The models presented in this study might be suitable for clinical use, supporting clinicians in their individualized assessment of expected survival in BC patients. They might also be used as a layered tool for clinical research.