The rapid growth of e-commerce, accelerated by the COVID-19 pandemic, has led to a significant increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. This study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. The review aims to identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud. The paper examines research published in the past decade, employing a content analysis of 101 publications to identify research gaps, recent techniques, and the increasing utilization of artificial neural networks in fraud detection within the industry. The review highlights common vulnerabilities on e-commerce platforms, common types of fraud, and the commonly used machine learning and data mining techniques for fraud detection. It also identifies research gaps, trends, and future research directions, particularly focusing on class imbalance issues in fraud data.The rapid growth of e-commerce, accelerated by the COVID-19 pandemic, has led to a significant increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. This study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. The review aims to identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud. The paper examines research published in the past decade, employing a content analysis of 101 publications to identify research gaps, recent techniques, and the increasing utilization of artificial neural networks in fraud detection within the industry. The review highlights common vulnerabilities on e-commerce platforms, common types of fraud, and the commonly used machine learning and data mining techniques for fraud detection. It also identifies research gaps, trends, and future research directions, particularly focusing on class imbalance issues in fraud data.