Fraudsters are attracted to the e-commerce sector because of the increasing transaction amounts, underscoring the crucial importance of efficient fraud prevention and detection mechanisms. This paper delves into e-commerce fraud detection through a systematic literature review, exploring the evolving landscape of advanced technologies, particularly machine learning automation. This technology enhances the detection capabilities for fraudulent activities by efficiently processing large volumes of e-commerce transaction data. As fraudulent transactions become more complex, there is a need for efficient ways to counteract these risks. To combat fraudulent transactions, there's a growing need for effective methods. Our primary objective is to comprehensively study various algorithms employed in e-commerce for fraud prevention. The survey focuses on comparing and assessing the effectiveness of this algorithm. The aim is to provide insight into the effectiveness of the proposed techniques in comparison to existing techniques. The findings contribute to a deeper understanding of the current state of e-commerce fraud detection, offering valuable insights for researchers, practitioners, and stakeholders in the ongoing battle against online fraud.