Reliable snow water equivalent and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we propose a Long Short-Term Memory neural network for operational use in snow hydrology. Once trained, it reduces computational cost by 70 percent compared to the Ensemble Kalman Filter, with a slight decrease in performances. This deep learning approach provides a scalable, efficient, and cost-effective modeling solution for hydrology.