The rapid rise of digital financial transactions has noticeably increased the risk of credit card frauds all over the globe, which poses major threats to monetary institutions and consumers. This paper presents a comparison of three machine learning models—Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), and Gated Recurrent Units (GRUs)—for detecting fraudulent credit card transactions. The study evaluates the models using four key performance metrics: Sensitivity, Specificity, Accuracy, and Error Rate. The findings reveal that the GRU model outperforms both ANN and RNN, achieving an impressive accuracy of 99.9%. These findings emphasize the capability of GRU in developing effective and consistent systems for detecting credit card fraud.