With the emergence of cryptocurrencies and Blockchain technology, the financial sector is turning its gaze toward this latest wave. The use of cryptocurrencies is becoming very common for multiple services. Food chains, network service providers, tech companies, grocery stores, and so many other services accept cryptocurrency as a mode of payment and give several incentives for people who pay using them. Despite this tremendous success, cryptocurrencies have opened the door to fraudulent activities such as Ponzi schemes, HYIPs (high-yield investment programs), money laundering, and much more, which has led to the loss of several millions of dollars. Over the decade, solutions using several machine learning algorithms have been proposed to detect these felonious activities. The objective of this paper is to survey these models, the datasets used, and the underlying technology. This study will identify highly efficient models, evaluate their performances, and compile the extracted features, which can serve as a benchmark for future research. Fraudulent activities and their characteristics have been exposed in this survey. We have identified the gaps in the existing models and propose improvement ideas that can detect scams early.