This investigation demonstrates the potential of modern machine learning and data analysis to identify credit card fraud, thereby ensuring the security of financial transactions. The proliferation of online payments and transactions has resulted in an increase in fraudulent activities. Subsequently, considerable losses were sustained by both individuals and organizations. Credit card transactions that are anomalous are identified by the methodology. Random Forest, Decision Trees, Neural Networks, and Logistic Regression are employed to classify transactions as either authentic or fraudulent. To improve the model’s accuracy, feature selection, standardization, and data purification are implemented. Transaction volume, frequency, location, and time are the variables that produce system recommendations. In order to identify unexpected events, anomaly detection techniques are implemented. The model’s precision, recall, accuracy, and F1-score can be determined by utilizing annotated datasets for training and evaluation. In experimental settings, rule-based fraud detection is outperformed by machine learning models. The proposed approach will reduce the number of false positives and expedite the identification of fraudulent transactions. Real-time surveillance of anomalous occurrences and automatic alerts are facilitated by this method.