Fraud detection in the context of financial transactions remains a critical measure to protect the business sector and consumers from considerable economic losses. This study emphasizes on evaluating the efficiency of different algorithmic rules of machine learning, including Decision Tree, K-Nearest Neighbors, Random Forest, Naïve Bayes, and XGBoost, for identifying fraudulent activities. To improve the dataset’s predictive power, an information gain-based feature selection technique is employed, ensuring the retention of only the most relevant features. The models are trained and validated through a cross-validation framework, and their performance is rigorously measured through diverse evaluation metrics. Furthermore, a detailed analysis is conducted using confusion matrices, precision-recall curves, ROC curves, learning curves, calibration curves, and cumulative gain charts. By examining these algorithms in depth, this study offers crucial insights into their respective advantages and limitations, offering a foundation for developing reliable, machine learning-driven solutions to enhance financial security.