This study investigates the prediction of bioenergy potential for agricultural residues in the European Union (EU) by 2050, focusing on France and Germany, using machine learning techniques. A data set including diverse factors influencing wheat, maize, and barley production was established to develop an Artificial Neural Network (ANN) model. Key factors including climate conditions, fertilizers, population, and trade were considered to capture the past trends and correlations to the bioenergy production. The model’s performance was evaluated using metrics such as R-squared, RMSE, and MAPE. Results showed correlations in the model and potential increases in the total bioenergy production from the studied crops’ residues by 2050, with a total of 2605 PJ in the EU. Comparisons with other studies were made to validate our findings. Limitations of the current model are discussed, with lack of factors that include potential future technological advancements in the agriculture sector, and socio-economic factors. For future research in this area, it is suggested using a more comprehensive dataset, taking more factors into account, and creating scenario-based results, to enhance the predictive accuracy of future models