A nation’s economic, social, and national security are all severely affected by variations in crude oil prices, which is a basic energy source. Research on accurately forecasting price changes for crude oil is always progressing. This research presents a forecasting strategy for crude oil pricing using artificial neural networks. The presented model uses standardization techniques to prepare the historical data for the subsequent processes. It is possible to predict future prices by using a Feed Forward Neural Network (FFNN) with four layers. West Texas Intermediate (WTI) and Brent crude oil prices are utilized on a daily, weekly, and monthly basis to demonstration and confirmation. Directional statistic, accuracy of prediction, the model is evaluated using root mean square error and mean absolute error expressed as percentages. Empirical findings confirm that the suggested approach performs better than any of the previous approaches. Additionally, it is noted that the presented method achieved higher prediction in contrast to other methods.