Breast Cancer is a common cancer occuring among women throughout the entire world. This attributes to a majority of deaths and cases in the global statistics, which makes it a significant health problem in our society. This paper showcases the classification of breast cancer tumors and the different boosting/ensemble techniques which are used for a more accurate classification. This research presents the working and comparison between classifiers such as Bagging Meta-Estimator, ADA-Boost and XGBM. We will also be showcasing the comparison between the results of these classifiers/techniques and conclude which one is the most accurate in the classification process. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.