The need for effective precise scalable solutions emerges as a key problem in detecting online payment fraud aimed at protecting monetary assets of both consumers and institutions. The research seeks to develop FraudMonitor which represents a dependable fraud detection system based on modern machine learning approaches that achieves precise fraud identification and stopping of online payment fraud activities. XGBoost algorithm demonstrates superiority in gradient-boosting approaches and achieves great computational efficiency and handles both large-scale and unbalanced datasets through its reputation in the industry. Online fraud detection implements XGBoost because it detects complex patterns and delivers superb real-time forecasting results. The current models implementing XGBoost in online fraud detection systems prove effective by reaching detection rates between 94–97% on authentic datasets. The proposed model constructs an enhanced XGBoost solution through optimized hyperparameter optimization combined with complex feature engineering to enhance its general performance. This model achieves superior efficient accuracy performance relative to standard practices which leads to decreased false positives and negatives in fraud detection assessment. Through its application on multiple digital payment systems FraudMonitor offers reliable transaction security which builds trust in online financial transactions. The real-time solution FraudMonitor provides companies with a scalable yet efficient system that detects payment fraud risks in the online realm.