The rising amount of digital financial transactions has brought about in a growth in fraudulent activity, consequently compromising users and financial institutions significantly. More complicated methods are needed since conventional rule-based fraud detection systems find it difficult to keep up with changing fraud schemes. In addition XGBoost, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Random Forest (RF) machine learning (ML) models in real-time fraud detection is looked at in this paper. These models are trained and tested on a vast dataset comprising over six billion transactions. With 100% accuracy, precision, and recall, the RF model surpasses all others, based to the results, hence it is the most dependable method for identifying fraudulent transactions with least false positives. Important difficulties in ML-based fraud detection such as handling imbalanced datasets, modifying to new fraud techniques, and increasing computational efficiency for real-time processing also are covered in this paper. Furthermore suggested is an ensemble-based method using many ML methods to improve fraud detection accuracy. The findings show that artificial intelligence-driven fraud detection offers a scalable and quick fix for recent banking systems, consequently considerably improving financial security. Deep learning algorithms, real-time adaptive learning, and improved data integration techniques should all be investigated in future work to maximize fraud detection capacity.