In this study, an integrated artificial neural network (IANN) model incorporating both observed and predicted time series as input variables conjoined with wavelet transform for flow forecasting with different lead times. The daily model employs forecasts of the tributaries in its input structure in order to predict the daily flow in the main river in the next time steps. The predictive models for the tributaries are those of the conventional wavelet-ANN models in which they comprised only observed time series as input variables. The monthly model updates its input structure by other forecasts of the tributaries and also the predicted time series of the main river in the previous time step. The model is utilized for flow forecasting in the Snoqualmie River basin, Washington State, USA. In the integrated model, the output of each tributary (sub-basins) and also the previous flow time series of the main river are used as input variables. Regarding the results of this study, the daily flow discharge can be successfully estimated for up to several days ahead (4 d) in the main river and tributaries. Moreover, an acceptable prediction of the flow within the next two months can be achieved by applying the proposed model.