This research paper provides a concise but comprehensive study of boosting algorithms as an effective solution for class imbalance issues. It seeks to address the ongoing problem encountered in machine learning algorithms that is class imbalance in the datasets, and more specifically, evaluate the efficiency of boosting algorithms to counteract this issue. The findings are collected by following the procedure which includes the adoption of the SMOTEEN and various boosting algorithms carefully. The research paper concludes with a finding that various boosting methods for example XGBoost, Light GBM, and CatBoost had the potential to manage complicated imbalanced data. All boosting algorithms are subjected to a diverse selection of assessment criteria including accuracy, precision, and recall for instance. Among all, Light GBM shines bright, by managing to strike a balance at both precise and scalable prediction performance. The process of analysis, findings and conclusion, therefore, becomes an essential tool for practitioners who want to address the problem of skewed data class, enabling them to get better performance and reliability through machine learning.