Life expectancy prediction using machine learning can support public health planning and policy making, yet practical guidance on selecting among ensemble methods for population-level modeling remains limited. This study evaluates four ensemble regressors, Random Forest, XGBoost, LightGBM, and Gradient Boosting, for global life expectancy prediction under a unified experimental protocol. We use a dataset of 22,050 observations from 195 countries with 149 health and socioeconomic indicators. Models are tuned via extensive hyperparameter search with stratified cross-validation, and computational efficiency is assessed alongside predictive accuracy. Performance is reported using the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE), both overall and stratified by country income group. XGBoost achieves the highest test accuracy (R2 = 0.9943, RMSE = 0.7386 years, MAE = 0.4448 years), outperforming Gradient Boosting (R2 = 0.9908), LightGBM (R2 = 0.9860), and Random Forest (R2 = 0.9856). Performance remains strong across income strata, although lower accuracy in low- and lower-middle-income settings suggests heterogeneous predictability across development contexts. Overall, XGBoost provides the best accuracy-efficiency trade-off under the adopted country-year split, while country-held-out and temporal validation are required before claiming full out-of-country generalization.