Accurate estimation of suspended sediment in rivers is very important for designing and operation of water resources projects. Sediment estimation by conventional methods like rating curves don’t provide accurate results. In this paper, gene expression programming (GEP) model which is an extension of genetic programming (GP) technique, was used to estimate suspended sediment in the river. The GEP results were compared with those of the adaptive neuro-fuzzy, neural networks and rating curve models. In this regard, the streamflow and suspended sediment data from Vanyar station that located on Aji-chay river in East- Azarbaijan province are used. The root mean square errors (RMSE) and determination coefficient (R2) statistics were used to evaluate the accuracy of the models. The results showed that the GEP model had better performance than other considered models in estimating suspended sediment. The relative RMSE difference for the test period between GEP and ANFIS-Grid Partitioning, ANFIS-Sub Clustering, ANN and rating curve methods were 8, 10, 13 and 21%, respectively. The R2 values for GEP, ANFIS-Grid Partitioning, ANFIS-Sub Clustering, ANN and rating curve methods were 0.93, 0.84, 0.88, 0.86 and 0.81, respectively.