Predicting streamflow values accurately is vitally important for hydrology studies. Two heuristic models, namely, gene expression programming (GEP) and support vector machine (SVM) are used and assessed utilizing data from four stations in China. The k-fold testing for local and external data management scenarios are tested extensively. Results indicate that models with inputs of current and one previous day's streamflow records provided the best accuracy. Both the GEP and SVM models can predict accurate streamflow values with respect to the observed records. GEP performed better than the SVM in all k-fold testing stages with lower skewness and standard deviation values for streamflow records. The test accuracy demonstrated high variations for the local and external k-fold case which proved the necessity of k-fold testing or data scanning procedure in daily streamflow prediction. Daily streamflow of downstream stations was also estimated using the data of upstream stations (external k-fold). The best results were obtained by the models trained using the data from the nearest upstream station. In some cases, the accuracy of the external models was found to be comparable to local models. This suggested the use of external models in streamflow prediction in the case of data scarcity.