Accurate estimation of sediment inflow into reservoirs is essential for sustainable water resources management, reservoir operation, and long-term storage preservation. However, conventional sediment rating curve approaches are often associated with considerable uncertainty due to hydrological variability, limited sediment observations, and the nonlinear behavior of sediment transport processes. This study aims to evaluate the performance and uncertainty of seven sediment rating curve methods, including the USBR, FAO-USBR, Ferguson-USBR, Category Average, FAO-Category Average, Ferguson-Category Average, and Wet–Dry Period Separation methods, using 51 years of hydrological and sediment data from the Telezang hydrometric station upstream of Dez Dam, Iran. To quantify uncertainty in sediment volume estimation, two stochastic approaches, namely Monte Carlo Simulation (MCS) and Latin Hypercube Sampling (LHS), were implemented in the Python programming environment (NumPy and SciPy libraries). Probability distributions were assigned to the principal uncertain inputs, namely the annual discharge, the sediment-rating curve coefficients (sampled as correlated pairs) and the sediment composition (subject to a closure constraint). The estimated reservoir sedimentation obtained from the different rating curve methods was further compared with available hydrographic survey data from the Dez Dam reservoir. The results indicated that the USBR, Category Average, Ferguson-corrected Category Average, and Wet–Dry Period Separation methods produced estimates more consistent with the available hydrographic observations. Uncertainty analysis revealed that sediment-rating curve coefficients constituted the dominant source of uncertainty, followed by flow discharge, whereas sediment composition contributed the least to the overall uncertainty. With 10,000 realizations in each approach, the overall coefficients of variation of the cumulative sediment volume estimated by MCS and LHS were 0.42 and 0.41, respectively, while LHS converged with fewer realizations. The findings demonstrate the importance of integrating stochastic uncertainty analysis with sediment rating approaches to improve the reliability of reservoir sedimentation assessments.