The field of hydrological modeling and precipitation prediction is witnessing significant advancements driven by innovative deep learning techniques. Recent developments emphasize the integration of multi-task learning and hierarchical modeling to better capture the complex, interdependent processes governing streamflow and precipitation patterns. These approaches leverage domain-specific causal knowledge and exogenous data to enhance both the accuracy and interpretability of predictions. Notably, the use of latent diffusion models for precipitation nowcasting and hierarchical conditional multi-task learning for streamflow modeling represent groundbreaking methodologies that address longstanding challenges in spatial detail and causal relationship capture. Additionally, semi-shared machine learning architectures like Hydra-LSTM are being employed to improve prediction accuracy across diverse watersheds by allowing for the incorporation of catchment-specific data without compromising model generality. These advancements collectively push the boundaries of what is possible in hydrological forecasting, offering new tools for efficient water resource management and disaster mitigation.