This study presents a real-time fraud detection system for online payment platforms, leveraging machine learning techniques to identify suspicious transactions. The system analyses historical transaction data to uncover patterns commonly associated with fraudulent activity. By applying algorithms such as decision trees, random forests, and logistic regression, it distinguishes between legitimate and fraudulent transactions. The system offers both user and admin interfaces: users can securely transfer funds and review their transaction history, while admins can monitor transactions and manage potential threats. Experimental results demonstrate high accuracy in fraud detection, effectively reducing false positives and issuing real-time alerts. This model, when integrated into online payment systems, enhances security and boosts user confidence in digital transactions.