The widespread acceptance of digital payments especially the UPI in recent years has revolutionized finance in India, providing unparalleled convenience and efficiency. However, this growth has also been accompanied by a rise in financial fraud, creating serious challenges for the banking sector. This chapter provides a comprehensive analysis of various types of financial frauds related to the Unified Payments Interface (UPI) using detailed data to identify fraud through various machine learning (ML) techniques. The research concentrates on cutting-edge learning methods, such as deep learning models and machine learning models, to enhance knowledge acquisition and to detect fraudulent activities with high accuracy and low error. The study also explores vulnerability and behavioral analysis that will lead to fraud. The study uses extensive data to evaluate the effectiveness of these methods and provide insight into their validity and reliability. The study results show that machine learning can also detect fraud in digital payments and reduce financial fraud. The study highlights the importance of using advanced training methods in preventing financial transactions and demonstrates their capabilities to solve the complex problems of money fraud in the digital age. Overall, the study highlights the need for continued innovation and advanced fraud detection methods to guarantee the safety and reliability of digital transactions in India. This is important not only to maintain customer trust, but also to support rapid growth and the use of future technologies.