In the era of an energy crisis, reliable and robust Solar Radiation (SR) prediction has become an integral part of thermal systems for renewable and clean energy production. Machine learning models are widely used as a precise effective solution in SR fo9rcasting. In this paper, Artificial Neural Network (ANN) is employed to predict hourly SR. An open-source dataset available from the NASA hackathon task is used for evaluations. The models' effectiveness is evaluated using several evaluation metrics, and its efficiency is compared with Logistic Regression (LR) and AdaBoost models. The outcomes indicate that ANN model has a higher ability for predicting SR than LR and AdaBoost in the training and test stages.