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Credit card fraud detection has emerged as a critical application domain for machine learning, driven by the exponential growth of digital payment systems and increasingly sophisticated fraud schemes. This comprehensive review analyzes the current state of machine learning techniques for detecting fraudulent credit card transactions, examining multiple research papers from several academic databases. Through systematic analysis of recent literature, we explore traditional and emerging methodologies, evaluate their effectiveness, identify key chal- lenges including class imbalance and concept drift, and propose future research directions. Our findings indicate that ensemble methods and gradient boosting dominate current practice, while deep learning and hybrid approaches show promise for novel fraud patterns. The field faces persistent challenges in real-time processing, evolving fraud tactics, and limited public datasets. This review provides researchers and practitioners with a com- prehensive understanding of the current landscape and identifies promising avenues for future development