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Credit Card Fraud Has Emerged As One Of The Most Significant Challenges In The Digital Economy, With Fraudulent Transactions Causing Massive Financial Losses And Undermining Customer Trust In Financial Institutions. Traditional Rule-based Fraud Detection Systems Often Fail To Adapt To The Evolving Strategies Of Fraudsters, Resulting In Delayed Or Inaccurate Detection. To Address This Limitation, Machine Learning As A Data Mining Technique Provides A Promising Approach By Uncovering Hidden Patterns, Anomalies, And Correlations Within Large-scale Transaction Datasets. This Work Explores The Application Of Supervised And Unsupervised Learning Models—such As Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, And Clustering Methods—for Detecting Fraudulent Credit Card Transactions. Emphasis Is Placed On Handling Challenges Like Class Imbalance, Real-time Detection Requirements, And Model Interpretability. By Integrating Advanced Feature Engineering, Resampling Techniques, And Ensemble Learning, The Proposed System Aims To Improve Accuracy, Precision, And Recall In Fraud Detection While Minimizing False Positives. This Research Highlights The Potential Of Machine Learning To Create Adaptive, Efficient, And Scalable Solutions, Ultimately Strengthening Security In Electronic Payment Systems.