In the increasingly digitized landscape of financial transactions, the rise in credit card fraud poses a substantial threat to both individuals and institutions. This study addresses the challenge of accurate and timely detection of fraudulent credit card activities using a novel ensemble approach. The proposed method combines the power of Adaptive Boosting (AdaBoost) and Aggregate Voting to create a robust and effective fraud detection system. AdaBoost, known for its ability to enhance the performance of weak classifiers, is utilized as the foundation of our approach. Multiple weak classifiers, represented as decision trees with limited depth, are trained iteratively, each focusing on different aspects of the intricate patterns underlying fraudulent activities. The AdaBoost algorithm adapts by assigning higher weights to misclassified instances, ensuring an emphasis on accurately classifying challenging cases.