Online transactions face significant risks from credit card fraud, highlighting the importance of strong fraud detection mechanism. Machine learning (ML) algorithms are often employed for fraud detection, with practitioners comparing and analyzing different models designed to uncover the ideal strategy for their goals. An extensive study is detailed in this paper aimed at finding the most effective credit card fraud forecasting model. The study evaluates seven advanced supervised ML algorithms, such as Random Forest (RF), Logistic Regression, Gradient Boosting, Naive Bayes, AdaBoost, K-Nearest Neighbors (KNNs), and Support Vector Machines (SVMs). To address dataset imbalance, SMOTE was applied, enhancing the models’ ability to detect fraud effectively. The analysis shows that RF demonstrates better accuracy when compared to the other algorithms and reliability, while Gradient Boosting also shows competitive results. The findings underline the performance of Gradient Boosting and Random Forest, emphasizing them as the most reliable models for identifying credit card fraud.