In the era of digital transformation, the adoption of cashless transactions has increased significantly, with digital payment platforms such as Unified Payments Interface (UPI) in India becoming integral to the economy. However, this rapid shift has also led to a surge in fraudulent activities, necessitating robust mechanisms to identify and prevent fraud in real time. UPI fraud manifests in various forms, including phishing scams, fake payment requests, unauthorized transactions, and more sophisticated social engineering tactics. These fraudulent activities harm individuals by leading to financial losses and compromise trust in digital payment systems, which can ultimately hinder the adoption of cashless technologies. To counteract these threats, machine learning (ML) has emerged as a potent tool for fraud detection. ML leverages historical data, behavioral patterns, and transaction analysis to identify anomalies in real-time.