The study aimed to evaluate the ability of Artificial Neural Networks (ANN) to estimate the current monthly production of oil palm bunches using variables from the forest inventory, climatic elements, water deficit, soil and those related to the registration and management of plantations. The ANN estimated current production with a correlation above 0.6 and an average percentage relative error of around 13.0%, with the variables that gave the greatest contribution to modelling being those related to management, soil, genetic material and the accounting of mature bunches. Climatic variables were not as important, however, due to the influence of the climatic element on oil palm productivity, it is necessary to keep them in the modelling. The ANN demonstrated that it is capable of modelling oil palm production, characterised by high variability, opening opportunities for future studies, combining and using new variables to improve the accuracy of estimates using this tool.