Precisely forecasting oil field performance is essential in oil reservoir planning and management. Nevertheless, forecasting oil production is a complex nonlinear problem due to all geophysical and petrophysical properties that may result in different effects with a bit of change. All decisions to be made during an exploitation project needs to be made considering different efficient algorithms to simulate data, providing robust scenarios to lead to the best deductions. To reduce the uncertainty in the simulation process, researchers have efficiently introduced machine learning algorithms for solving reservoir engineering problems because they can extract the maximum information from the dataset. Accordingly, this paper proposes using a Random Forest model to predict the daily oil production of an offshore reservoir. In this study, the oil rate production is considered a time series and was pre-processed and restructured to fit a supervised learning problem. We use the Random Forest model to forecast a one-time step, which is an extension of decision tree learning, widely used in regression and classification problems for supervised machine learning. For testing the robustness of the proposed model, we use the Volve oil field dataset as a case study to conduct the experiments. The results indicate that the Random Forest model could adequately estimate the one-time step of the oil field production.