The objective of this work is to further explore the potential application of Machine Learning algorithms in production prediction and ultimate recovery. Intelligent Machine Learning Approaches such as Gradient Boosted Trees (GBT), Adaboost, and Support Vector Regressor (SVR) are applied to detect the most important features contributing to cumulative production prediction within the first 12 producing months. The models are applied on a data set composed of 5 wells in the Volve field in the North Sea. The collected data was then filtered and used to structure and train the different Regression algorithms and fine tune the appropriate hyperparameters. The different models were All models were evaluated by measuring the Mean Absolute Error (MAE). The generalization and precision performance of the proposed models are established by comparing the forecasted outcome after cross validation with field data. The optimized model can predict production response with high accuracy. The data-fitting process comprises of splitting the data into training using 70% of the data set, 15% validation, and 15% testing. Constructing a regression model on the training set and validating it with the test set. Recurrent application of a "cross-validation" process produces important information concerning the robustness of any regression-modeling method. Six parameters were considered as input factors to build the model. Factors affecting production prediction included on stream hours, average choke size, bore oil volume, bore gas volume, bore water volume, average wellhead pressure were used as input. The outcome showed that the developed model provided better prediction compared to analytical models with a 11.71% MAE prediction for SVR. This novel data mining application could be trained on any dataset to help predict future production performance at any conditions in any given scenario.