Credit card approval is a critical task for financial institutions that must balance the need for customer acquisition with risk management. Traditional rule-based methods typically lack the flexibility and adaptability of data-driven approaches. This paper presents a machine learning-based framework for predicting credit card approvals using various classification algorithms. Models such as logistic regression, decision trees, random forest, and gradient boosting are evaluated on a publicly available dataset. Performance is measured using accuracy, precision, recall, and F1-score. Results show that machine learning models significantly enhance approval prediction performance and offer valuable insights into feature importance.