Accurate assessment of suspended sediment load (SSL) in rivers plays a vital role in the planning and management of water resource structures. This study focused on the assessment of different techniques for the estimation of SSL in rivers. This comprises sediment rating curves (SRC) and soft computing techniques such as artificial neural networks (ANN), hybrid wavelet-coupled artificial neural networks (WANN), and local linear regression (LLR) models. These techniques were employed to estimate the daily SSL at Azad Pattan station of the Jhelum River in Pakistan. Further, the Gamma and M-test were performed to select the best-input variables and appropriate data length for smooth model development. By evaluating the outcomes of all the leading models, it can be concluded that the performance of soft computing models is superior to the SRC approach for the SSL estimation. This is because the soft computing models employed a non-linear approach for the data reconstruction. Additionally, the WANN was the most precise model to predict the SSL. Thus, WANN models are a powerful technique to reconstruct the SSL time series because they reveal the salient characteristics enclosed in the SSL time series.