Credit card fraud, a pervasive and costly issue, has become increasingly sophisticated as technology advances. Detecting fraudulent transactions promptly is crucial to safeguarding both consumers and financial institutions. This paper explores the challenges and potential solutions associated with credit card fraud detection. The increasing prevalence of fraudulent transactions poses a significant threat to the financial security of individuals and businesses. The increasing prevalence of fraudulent transactions poses a significant threat to the financial security of individuals and businesses. Developing robust and efficient fraud detection systems is essential to mitigate the financial losses associated with fraudulent activity. This research aims to develop a novel machine learning model that can accurately identify fraudulent transactions in real-time. By incorporating advanced techniques such as deep learning and anomaly detection, the proposed model seeks to improve the accuracy and efficiency of fraud detection. The goal is to provide a valuable tool for financial institutions to protect their customers from financial losses due to fraudulent activity. Our proposed machine learning model achieved a high accuracy rate in distinguishing between legitimate and fraudulent transactions, outperforming traditional rule-based methods. The implementation of real-time fraud detection capabilities significantly reduced the financial losses incurred by the financial institution.