Our study compares neural network models for predicting discharge in ungauged basins. We evaluated convolutional neural networks (CNNs), long short-term memory (LSTM) and gated recurrent units (GRUs) using 28 years of weather data. CNNs showed the best accuracy, while GRUs were faster and nearly as accurate. Adding static features improved all models. The research enhances flood forecasting and water management in regions lacking direct measurements, offering efficient and accurate predictive tools.