A Bus Rapid Transit System (BRTS) with earmarked lanes potentially provides efficient public transportation, and helps in controlling urban traffic congestion. While travel time prediction (TTP) is essential in a BRTS, existing algorithms generally assume GPS logs available at short uniform intervals. However, those are rarely evaluated on BRTS in emerging economies, where logged GPS data could be available at sparse nonuniform intervals. To fill the gap, we study the efficacy of certain well known ML models, namely, Random Forests (RF), Light Gradient Boosting (LGB), and Extreme Gradient Boosting (XGBoost, XGB) in utilizing historical data. Performance of those ensemble learning methods is compared with that of a conventional travel time prediction (CTTP) method, which uses historical averaging. It was found that XGB was superior to other methods at hand, and the prediction error by approximately 60% compared to the CTTP method. Alongside improving the experience of commuters, the proposed XGB-based TTP method also improves the estimation of intersection crossing time (ICT), which potentially leads to efficient traffic policy making. © 2021 IEEE.