This Workshop aims at providing a forum for members of the research and academy community. The Workshop enables the participants to understand the recent technologies in ML Foundations, ensemble techniques such as Boosting, Voting, and Stacking, along with their practical implementation using Python. The Workshop aims to provide participants with a comprehensive understanding of the fundamental concepts, advanced methodologies, and practical applications of ensemble learning. The workshop will introduce the principles of ensemble modeling Participants will explore major ensemble techniques, including Bagging, Random Forest, Boosting, Gradient Boosting, XGBoost, LightGBM, CatBoost, Voting Classifiers, and Stacking.