Credit card fraud is an escalating problem in the digital age, where transactions are increasingly conducted online. The convenience of credit cards comes with the inherent risk of fraud, leading to significant financial losses for consumers, businesses, and financial institutions. Traditional methods of fraud detection, primarily rule-based systems, have proven to be insufficient in effectively identifying fraudulent activities due to their static nature and inability to adapt to evolving fraud patterns. This necessitates the development and implementation of more sophisticated and adaptive techniques. Machine learning, with its ability to analyze large volumes of data and detect complex patterns, presents a promising solution to this problem. Machine learning (ML) algorithms have the potential to revolutionize credit card fraud detection by offering a dynamic, data-driven approach to identifying fraudulent transactions. Unlike traditional rule-based systems, machine learning models can learn from historical data, adapt to new fraud patterns, and improve their performance over time. This adaptability is crucial given the constantly changing tactics of fraudsters. Various machine learning techniques, including supervised and unsupervised learning, are employed to detect anomalies and predict fraudulent behavior. Supervised learning models, such as logistic regression, decision trees, random forests, and support vector machines, are trained on labeled datasets where transactions are marked as either fraudulent or legitimate. These models learn the characteristics of fraudulent transactions and can predict the likelihood of new transactions being fraudulent based on the learned patterns. On the other hand, unsupervised learning models, such as clustering and anomaly detection algorithms, do not require labeled data. They identify outliers in the data that deviate from the norm, which may indicate potential fraud.