This paper addresses the challenge of credit card fraud detection by proposing an ensemble machine learning model that integrates Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. The model aims to tackle the limitations of current fraud detection technologies, such as data imbalance, concept drift, false positives/negatives, limited generalizability, and real-time processing challenges. To address data imbalance, the model employs under-sampling and the Synthetic Over-sampling Technique (SMOTE). The evaluation is conducted using a dataset of European credit card transactions, which exhibit significant skewness with only 0.172% of transactions being fraudulent. The proposed model's performance is assessed using metrics such as accuracy, precision, recall, and F1-score. The results show that the ensemble model outperforms individual machine learning models and traditional methods in terms of these metrics, demonstrating its effectiveness in mitigating credit card fraud. The paper also discusses the computational efficiency of the ensemble models, ensuring they can handle complex algorithms and large datasets without compromising speed. The findings provide valuable insights for developing more resilient and adaptive fraud detection systems.This paper addresses the challenge of credit card fraud detection by proposing an ensemble machine learning model that integrates Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. The model aims to tackle the limitations of current fraud detection technologies, such as data imbalance, concept drift, false positives/negatives, limited generalizability, and real-time processing challenges. To address data imbalance, the model employs under-sampling and the Synthetic Over-sampling Technique (SMOTE). The evaluation is conducted using a dataset of European credit card transactions, which exhibit significant skewness with only 0.172% of transactions being fraudulent. The proposed model's performance is assessed using metrics such as accuracy, precision, recall, and F1-score. The results show that the ensemble model outperforms individual machine learning models and traditional methods in terms of these metrics, demonstrating its effectiveness in mitigating credit card fraud. The paper also discusses the computational efficiency of the ensemble models, ensuring they can handle complex algorithms and large datasets without compromising speed. The findings provide valuable insights for developing more resilient and adaptive fraud detection systems.