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In the era of digital banking, the volume of online transactions has grown exponentially, accompanied by an alarming increase in fraudulent activities. Traditional rule-based systems are limited in scope, static in nature, and incapable of adapting to ever-evolving fraud tactics. To overcome these limitations, this project proposes a Machine Learning-based Fraud Detection System capable of analyzing historical transaction data, identifying anomalies, and predicting fraudulent behavior with improved precision and efficiency. The system leverages supervised machine learning models such as Logistic Regression, Random Forest, and XGBoost, enhanced through data preprocessing and class-imbalance handling techniques like SMOTE. The models are evaluated on key metrics such as accuracy, precision, recall, F1-score, and confusion matrix to ensure robustness. A user-friendly interface is designed using web frameworks such as Flask or Streamlit, enabling real-time and batch detection of fraudulent transactions. The deployable and scalable architecture ensures seamless integration with existing banking infrastructure. By significantly reducing false positives and enhancing fraud detection accuracy, the proposed solution not only minimizes financial risks but also strengthens customer trust and confidence in digital banking