UPI or Unified Payments Interface is extremely rapid in terms of growing acceptance regarding digital payments, and it also increases the cases of fraud. Traditional systems for discovering fraud utilize static rules and two-factor authentication. As a result, they cannot be advanced to detect complex and evolving patterns of fraud. They also generate frequent false positives for transactions that do get falsely classified as fraudulent, which means delayed processing and user dissatisfaction. This idea refers to the Advanced UPI Fraud Detection System which employs machine learning to analyse transaction patterns and user behaviour in real-time. Use models like decision trees and random forests or even neural networks in learning to spot fraudulent activities. The system will keep learning new data and will be able to detect emerging patterns of fraud easily. It analyses user behaviour alongside real-time detection. Hence it enhances the UPI security and offers a great faster turnaround time and accuracy than rule-based systems.