With increased use of Unified Payments Interface (UPI) transactions, fraudulent transactions have witnessed a sudden hike, which has been a welcome challenge for financial security. This project would develop an efficient fraud detection system with the help of machine learning and deep learning. Various classification techniques are proposed, ranging from Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, XGBoost, and Convolutional Neural Network (CNN) to classify fraudulent transactions most efficiently. The data are subjected to intensive preprocessing, analysis, and then used to train the models with performance tested on the basis of accuracy, precision, recall, and F1-score. The best-performing model will be implemented in a web application with Flask, which is scalable, and fraud detection is possible in real-time. The primary aim of the research is to improve the security of electronic payment systems by offering an efficient and timely defense against financial fraud attacks.