Fraud detection has been a serious problem in a variety of industries such as banking, medicine, insurance, and many others. Fraudulent activities have increased along with the number of online transactions made using various payment methods, including credit/debit cards, PhonePe, Gpay, Paytm, etc. Fraudsters have also developed very sophisticated escape routes so they can make their willing successful. To avoid this kind of problems, a secure system is required for authentication and preventing customers from fraud. Nowadays, fraud detection has become a difficult task because no system is faultless and there is always a vulnerability. Therefore, machine learning algorithms for fraud detection are very helpful in reducing fraud. In this paper, we propose an efficient machine learning approach employing models like XGBoost, AdaBoost, and ensemble models to detect fraudulent credit card transaction. The proposed model for credit card fraud detection exhibits superior performance than the existing systems.