Hepatorenal syndrome (HRS) is a life-threatening complication of cirrhosis with a poor prognosis. To develop novel and effective nomograms which could numerically predict both the hospital survival and transplant-free survival of HRS, we retrospectively enrolled a cohort of 149 patients. A backward stepwise method based on the smallest Akaike information criterion value was applied to select the covariates to be included in the Cox proportional hazards models. The Harrell C-index, area under the receiver operating characteristic curve (AUC), Brier score, and Kaplan-Meier curves with the log-rank test were used to assess nomograms. The bootstrapping method with 1000 resamples was performed for internal validation. The nomogram predicting hospital survival included prothrombin activity, HRS clinical pattern, Child-Pugh class, and baseline serum creatinine. The C-index was 0.72 (95% confidence interval (CI), 0.65-0.78), and the adjusted C-index was 0.72 (95% CI, 0.66-0.79). The nomogram predicting transplant-free survival included sex, prothrombin activity, HRS clinical pattern, model for end-stage liver disease-Na score, and peak serum creatinine. The C-index of the nomogram was 0.74 (95% CI, 0.69-0.79), and the adjusted C-index was 0.74 (95% CI, 0.68-0.79). The AUC and Brier score at 15, 30, and 45 days calculated from the hospital survival nomogram and those at 6, 12, and 18 months calculated from the transplant-free survival nomogram revealed good predictive ability. The two models can be used to identify patients at high risk of HRS and promote early intervention treatment.