The nonlinear behaviour and parameter coupling inherent in gas turbine engines pose persistent challenges in the development of accurate thrust prediction models for unmanned aerial vehicle (UAV) propulsion systems. This study investigates the application of ensemble regression methods to address these complexities and enhance predictive accuracy using experimental data obtained under controlled test conditions. The evaluated models include bootstrap aggregating (bagging), adaptive boosting (AdaBoost), light gradient boosting machine (LightGBM), CatBoost, stacked generalisation (stacking) and a voting regressor ensemble. Each model was trained and evaluated using experimentally measured thermodynamic and operational parameters across a range of engine operating conditions. Cross-validation results revealed that stacking achieved the lowest prediction errors with a root mean square error (RMSE) of 0.0635 and a mean absolute error (MAE) of 0.0442, while AdaBoost demonstrated a very high cross-validated coefficient of determination (${R^2}$) of 0.9998. CatBoost achieved ${R^2}$ values above 0.99, and voting produced ${R^2}$ values close to 0.99. LightGBM exhibited comparatively higher prediction errors and greater variability across folds. These results indicate the potential effectiveness of stacking and adaptive boosting approaches for thrust estimation in UAV gas turbine propulsion systems, while also demonstrating that ensemble performance depends strongly on algorithm selection and dataset characteristics.