Tax is the backbone of the country to provide public service and build infrastructure for the people in need. But most taxpayers in Ethiopia have a failure of willingness to pay expected tax for the tax authority. Since most taxpayers are not compliant, in order to investigate taxpayers' compliance level, the Ethiopian Ministry of Revenues (MOR) has developed a criterion for measuring taxpayers' compliance level by analyzing the taxpayers' data in Jun 2019. The main goal of this taxpayer‟s compliance level classification is that, recognizing and awarding the status of high-compliance taxpayers while influencing or mitigating fraud of tax from low-compliance level taxpayers. Since this new taxpayers‟ compliance level classification method is developed by MOR in 2019 for the first time, existing studies doesn‟t address. However, even if identifying taxpayers' compliance level is good for the next step to take action, the taxpayer compliance level identification criteria are complex and broad, and it can be tedious and erroneous to analyze a large number of taxpayers‟ data without the support of advance computing technology. To assist manpower experts and to help mitigate this problem, an assistive model that can predict taxpayers' compliance level from the provided data should be developed. The aim of this study is to create an assistive model using machine learning techniques that can learn from training data and that can predict taxpayer compliance levels to assist the ministry of revenue experts more efficiently and effectively so that it helps to mitigating tax evasion. This study is conducted using experimental design process approach. In this research we used machine learning approaches to construct a taxpayer compliance level prediction model using the Anaconda Python programming environment and the Jupyter IDE. As the result, we have tested four classification algorithms and we found accuracies 96.74%, 96.09, 99.67% and 79.15% for Random forest, K-Nearest Neighbour, Support Vector Machine, and Naive Bayes classifiers respectively. And we have found Support Vector Machine best performing algorithm with an accuracy of 99.67% after SMOTE. Keywords: Compliance Level, Machine learning, SMOTE, Taxpayers