Hydraulic pumps are key drivers of fluid power-based machines and demand high reliability during operation. Internal leakage is a key performance deteriorating fault that reduces pump’s efficiency and limits its predictability and reliability. Thus, this article presents a methodology for detecting internal leakage in hydraulic pumps using an unbalanced dataset of its drive motor’s electrical power signals. Refined composite multiscale dispersion and fuzzy entropies along with three statistical indicators are extracted and followed by second-order polynomial-based features. These features are normalized and visualized using partial dependence plot (PDP) and individual conditional expectation (ICE). Subsequently, ten machine learning classifiers are trained using four features, and their statistical hypothesis test is performed using a 5 × 2 paired t-test cross-validation for p < 0.05. Subsequently, top four performing classifiers are optimized using grid and random search hyperparameter optimization techniques. Due to slight difference in their accuracies, an ensemble of three best-performing algorithms is trained using the majority voting classifiers (MaVCs) for three splitting ratios (80:20, 70:30, and 60:40). It is demonstrated that MaVC achieves the highest leakage detection accuracy of 90.91%.