A Ponzi scheme is an investment fraud in which existing investors are paid with funds collected from new investors, which causes significant financial losses. This fraudulent activity also exists in blockchain-enabled platforms, but it can be detected and prevented through the application of machine learning techniques. This paper aims to identify and report solutions to detect and prevent Ponzi schemes on blockchain-enabled platforms. The research follows a Systematic Literature Review methodology following the PRISMA 2020 guidelines. The data collected during this study was recorded and stored in the Open Science Framework, ensuring transparency, reproducibility, and supporting future research endeavors. In the end, 49 papers were identified through a process of screening and snowballing. These papers are further studied to report publication trends, applied algorithms, and the reported challenges. The findings indicate a rising global trend in the use of machine learning to detect and prevent Ponzi schemes, particularly since 2017, with China and India leading the way. Ethereum and Bitcoin are the most frequently utilized platforms, while the combination of Support Vector Machine, Random Forest, and Extreme Gradient Boosting emerges as the effective approach. Data imbalance, data quality issues, and computational limitations are identified as key challenges in this field.