– Credit card fraud, an escalating challenge in the digital era, poses significant financial implications for businesses and compromises the security of consumers. Traditional rule-based systems, although effective to a degree, often fall short in detecting sophisticated fraud schemes. This review delves into the applicability and advantages of employing machine learning (ML) techniques, specifically anomaly detection, to mitigate the threat of credit card fraud. Anomaly detection, with its ability to identify unusual patterns in large datasets, offers a more proactive and adaptive approach to fraud prevention. As we navigate through the vast array of literature, it becomes evident that innovations in this domain are burgeoning, from the use of auto encoders, hybrid models, to cutting-edge feature selection methodologies. However, challenges persist, especially in addressing the imbalanced nature of fraud data and the dire need for real-time detection mechanisms. This review culminates in emphasizing the transformative potential of ML-driven anomaly detection, suggesting that its continuous evolution could pave the way for a more secure financial transaction environment in the imminent future.