<p>The Cau River Basin in Vietnam has recently faced increasingly frequent and severe floods, underscoring the urgent need for improved hydrological prediction and management strategies. To tackle challenges posed by limited observational data, this study proposes a mutual learning framework to enhance deep learning model performance. This innovative approach enables paired models to exchange knowledge, improving generalization, stability, and predictive accuracy. Comparative experiments show that the proposed mutual learning model substantially outperforms machine learning techniques (i.e., Linear Regression, SVM, Random Forest, and Decision Tree) as well as deep learning models (i.e., DLinear, iTransformer, PatchTST, TiDE, TimeXer, TSMixer, WPMixer, MLP, CNN, and LSTM). Using streamflow data from 1997 to 2013, the mutual learning model (MLP-MLP) had the highest performance with an NSE of 0.75, surpassing baseline models. Sensitivity analysis showed that temperature and precipitation are both essential predictors with precipitation having a stronger impact on model performance. The evaluation of input window sizes indicated that Window 5 yields the optimal configuration, while larger windows reduce accuracy. These results demonstrate the potential of mutual learning for improving streamflow prediction in data-scarce regions and highlight its applicability for hydrological forecasting.</p>