This study aims to evaluate the accuracy of two deep learning models, gated recurrent unit (GRU) and long short-term memory (LSTM), for predicting daily suspended sediment load (SSL) in the Missouri River at Omaha, NE in the United States and compare the results with a traditional time series model, autoregressive integrated moving average (ARIMA) model. The study used daily data of SSL in the Missouri River at Omaha, NE from October 2008 to September 2018. The two deep learning models, GRU and LSTM, were applied to the data and their accuracy was compared with the ARIMA model. The models were trained and tested using a 70–30 split of the data and evaluated based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results showed that GRU had a better performance with a coefficient of determination of 0.871 and a root mean square error of 0.142, compared to LSTM’s coefficient of determination of 0.865 and root mean square error of 0.148. GRU also had a lower mean absolute error of 0.097 compared to LSTM’s mean absolute error of 0.101. The study concludes that both GRU and LSTM can be used effectively in SSL modeling. However, GRU requires fewer training constraints and has a faster performance with higher accuracy compared to LSTM, which may need additional data to improve accuracy. The results suggest that GRU can be a promising tool for daily SSL prediction in river basins.