In view of the above, the general objective is to develop and evaluate predictive models for river level forecasting using historical hydrological and pluviometric data from the Tanaro River basin in Italy, applying Perceptron, LSTM, and GRU neural networks, and comparing their performance with traditional benchmark models such as the Persistence Model for forecast horizons ranging from 1 to 24 hours ahead.This study is classified as applied, quantitative research with an experimental orientation focused on model creation, training, and evaluation for hydrological prediction.It uses statistical inference and machine learning techniques to evaluate historical water levels data in a bid to benchmark model performance under different conditions as well as forecast major events such as flooding.The research uses an empirical method because it applies real data sets and benchmark assessments to prove the proposed solutions in actual operational settings