This is a project on the eligibility prediction of credit cards using the advanced machine learning boosting algorithms in the dataset of various customer attributes. Four boosted up algorithms, namely CatBoost, AdaBoost, LightGBM, and XGBoost, are used in the process to enhance the accuracy of prediction. In the first step, four models are individually trained and tested separately on two sets of different train and test data, then their respective accuracies are compared. The overall best model was XGBoost, which was observed to have a training accuracy of 93 percent and an accuracy of 85% on the test set. This was followed by LightGBM, CatBoost, and AdaBoost, which also exhibit quite competitive performance levels (and the test accuracies leveled off at around 85%). The goal of this project was to determine the optimal boosting model for determining credit card eligibility and to enlighten its application in financial decision-making systems.