<p>Traditional streamflow forecasting models based on machine learning often struggle to determine optimal parameters and achieve accurate predictions when applied to small and medium-sized rivers with limited data availability. To address this issue, this study proposes an Attention-based Bidirectional Long Short-Term Memory (A_BiLSTM) and, on this basis, develops a streamflow forecasting model based on model fusion, referred to as SFM_MF. The SFM_MF model employs Bayesian Linear Regression (BLR) to learn from small-sample data in the target basin, while leveraging the A_BiLSTM to model data transferred from hydrologically similar source basins. The final streamflow forecast is generated by integrating the outputs of BLR and A_BiLSTM through a weighted averaging method. Experimental results from the Jiulong River Basin and the Qinhuai River Basin demonstrate that A_BiLSTM outperforms baseline models, and that the SFM_MF model significantly surpasses its component models in terms of both predictive accuracy and generalization capability. Compared to the individual models, SFM_MF achieves reductions in root mean square error (RMSE) of 3.98% and 19.33%, respectively. These findings indicate that the SFM_MF model delivers superior forecasting performance for small and medium-sized basins with limited data resources.</p>