Online payment fraud has become a critical challenge in the digital economy, leading to substantial financial lossesand eroding consumer trust. The rise of web surfing and online shopping, so came the use of credit cards for onlinetransactions, as did the prevalence of online financial fraud. This study focuses on developing a machine learningbased system to detect and prevent fraudulent transactions in online payment platforms. The proposed solutioninvolves data preprocessing, feature engineering, and the selection of appropriate machine learning models such asLogistic Regression, XG Boost Classifier, Random Forests, and SVC. Given the imbalanced nature of the dataset,where fraudulent transactions are rare, advanced techniques are employed to enhance model accuracy. Theevaluation metrics include accuracy, confusion matrix. The system is designed for real-time deployment, offering arobust mechanism to reduce fraudulent activities and improve the security and reliability of online payment systems.