In this paper, the efficiency of a Wavelet-based Least Square Support Vector Machine (WLSSVM) model was examined for prediction of monthly Suspended Sediment Load (SSL) of the Aji-Chay River. First the SSL was predicted via ad hoc Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) models. Thereafter in hybrid models, streamflow and SSL time series were decomposed into sub-signals via a wavelet transform and the decomposed subseries were fed into LSSVMs and ANNs to simulate a discharge-SSL relationship. The results showed that ANNs led to better outcomes with Determination Coefficient (DC)=0.62 than ad hoc LSSVMs with DC=0.59. On the other hand, WLSSVMs performed better than wavelet-based ANN (WANN) models in monthly SSL prediction and wavelet data pre-processing could lead to more accurate results.