Oil and gas operators frequently rely on traditional methods to predict Estimated Ultimate Recovery (EUR) but, these methods often fail to accurately predict shale gas EUR. Therefore, machine learning (ML) algorithms were shown as promising alternatives but, the negative effects of imbalanced datasets on their performance still remains underexplored. This study addresses this gap with a Tree-Parzen Estimator (TPE) optimized Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) to alleviate the detrimental effects of the imbalanced datasets on model performance. Two cases were compared: one employed standard pre-processing while, the other employed TPE optimized SMOGN. Four ML algorithms: Artificial Neural (ANN), Deep-ANN, Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost) were trained across both cases with an imbalanced dataset of 460 Marcellus shale wells. Exploratory data analysis revealed that the imbalance was due to the evolution of completion techniques, leading to the underrepresentation of wells completed with more recent, aggressive methods. The proposed framework improved of the models from 0.8243 to 0.8934 to 0.8851–0.9186 with more significant gains in the underrepresented wells in the higher EUR regions ( ), where the improved from 0.2155 to 0.4598 to 0.5615–0.9472. The SMOGN enhanced SVR had the highest computational efficiency ( to train) and highest performance of 0.9472) in these higher EUR regions, while the SMOGN enhanced Deep-ANN had the highest overall performance ( of 0.9186). This framework outperforms standard pre-processing frameworks. Additionally, it enables operators to predict shale gas EUR more accurately for future infill wells completed with recent techniques, even while the wells are still producing in transient flow, facilitating early cost-saving decisions. To the best knowledge of the authors, this is the first research that proposed TPE optimized SMOGN to improve shale gas predictions.