It is an important task to predict medical waste (MW) estimation accurately for effective Waste Management System (VMS). The main aim of this study was to compare three ensemble machine learning algorithms to predict medical waste for İstanbul which is the biggest city in Turkey. There exists new machine learning (ML) algorithms called Ensemble Machine Learning Algorithms that have shown significant success in other disciplines yet have not been examined for MW. To bridge the literature gap, in this study, for the first time, a total of three ensemble machine learning algorithms: Random Forests (RF), Gradient Boosting Machine (GBM) and AdaBoost are developed to predict MW generation. To employ this study. 17-years real data were obtained from İstanbul Metropolitan Municipality Department Open Data Portal with the input variables namely number of hospitals, number of bed available at the hospital, crude birth rate and Gross Domestic Product (GDP). 80% of the total database being used for developing the models, whereas the rest 20% were used to validate the models In order to compare their performances, 5-fold cross-validation was applied and performance measures (MAE,RMSE and R-squared) were calculated in this study .Of the ensemble models, the RF model provided better performance than those of other models with RMSE, MAE, and R 2 of 1194.2, 898.12, 0.95, respectively, whereas the second best GBM accuracy with RMSE, MAE, and R 2 1290.76, 1160.43, 0.94, respectively. Although, CatBoost was interpreted as the efficient model for small datasets among the Machine Learning algorithms, was poorest accuracy with RMSE, MAE, and R 2 of 3349.57,2698.4,0.61. In addition, the findings revealed that GDP and number of hospitals were the most important inputs for the predicting MW generation using ensemble machine learning algorithms. These results will helpful for decision makers regarding both planning and designing medical waste management systems in the future facilities in the sense of sustainable management.