The Intelligent Credit Card Fraud Detection System is a machine learning–based solution designed to detect and prevent fraudulent credit card transactions in real time. With the rapid growth of online payments, digital banking, and e-commerce platforms, credit card fraud has become one of the most significant financial cybercrimes worldwide. Traditional rule-based fraud detection systems are often limited in identifying new and evolving fraud patterns.This project aims to develop an intelligent system that analyzes transaction data using machine learning algorithms to classify transactions as legitimate or fraudulent. The system uses supervised learning techniques such as Logistic Regression, Decision Tree, Random Forest, and Neural Networks to improve detection accuracy. The proposed system includes data preprocessing, feature engineering, model training, fraud prediction, and result visualization modules. The primary objective is to build a scalable, efficient, and cost-effective fraud detection system suitable for academic and small-scale financial applications. Future enhancements may include deep learning integration and real-time API deployment for banking environments