The present study addresses the problem of solar radiation prediction in the city of Nauta, considering its importance for applications in renewable energy and climate management. The main objective was to develop a predictive model based on LSTM artificial neural networks and the RandomForestRegressor machine learning algorithm, seeking to improve accuracy by reducing the root mean square error (RMSE) and increasing the correlation coefficient (R). The methodology included the collection of historical meteorological data from SENAMHI, which were preprocessed, normalized and segmented into training, validation and test sets. The models were configured and trained using machine learning techniques and recurrent neural networks. The results showed that the LSTM model obtained an RMSE of 25.94 and an R of 0.764 during training, while the RandomForestRegressor achieved an RMSE of 27.25 and a...