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Digitalisation of mechanical engineering necessitates the use of predictive tools that are explainable and allow for more rapid analysis. The study began with cold spraying (CS) of high entropy alloy (HEA) coatings, Al0.1-0.5CoCrCuFeNi and MnCoCrCuFeNi coatings at nitrogen gas temperatures of 650, 750, and 850 °C. The surface roughness, Ra, was measured using profilometry and microscopy techniques. The data set comprised 20 samples with varying experimental conditions and seven measured parameters (five input variables and surface roughness (Ra) as the output. The stacking ensemble structure included three base learners, Linear Regression (LR), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR) with RidgeCV as the meta-learner. LR was chosen due to its simplicity and interpretability, GPR was chosen due to its ability to capture nonlinear trends with small datasets, and RidgeCV was used to stabilize the model by shrinking coefficients and reducing variance. This ensemble was compared with XGBoost, which is a powerful single machine learning approach. Results indicated that the stacking ensemble performed better than XGBoost in all the regression metrics. XGBoost reached 96.79% accuracy, RMSE of 0.22 µm, MAPE of 3.32%, and R2 of 0.83, whereas the stacking ensemble achieved 97.26% accuracy, RMSE of 0.17 µm, MAPE of 2.73%, and R2 of 0.89. The results validate that stacking ensembles can improve predictive and robust performance even in scenarios of small data. These novel stacking-ensemble and analysis methods can be broadened for future applicability.