Predicting how much water flows in rivers is essential for managing our water resources and preparing for extreme events like floods. Current hydrological models, while vital for understanding the global water cycle, often struggle to provide accurate streamflow predictions at a local level, especially when they haven't been fine-tuned for each specific area. This fine-tuning, or calibration, is incredibly complex and time-consuming for large regions. Deep learning models, like LSTMs, show promise for streamflow prediction but typically don't offer insights into other crucial water variables, such as soil moisture. Our research introduces an innovative deep learning-based framework that acts as a post-processor. It integrates the outputs from traditional hydrological models with advanced AI, allowing us to significantly improve daily streamflow predictions without the need for extensive, basin-specific calibration.