Electric vehicles (EVs) are becoming more and more popular in many countries. The range prediction system is one of the most important systems in EVs. It helps provide important information for drivers to make appropriate routes, especially in developing countries where the charging station network is not yet dense. An Electric Vehicle Range Prediction system is a technology that utilizes machine learning algorithms to estimate the remaining driving range of an EV. The system considers various factors such as battery capacity, driving style and consumption to enhance the accuracy of range prediction and mitigate EV range anxiety. In this study, we propose an approach to build an Electric Vehicle Range Prediction system with multiple parameters consideration. Models can be embedded into electric vehicles or into stand-alone applications to help drivers plan their routes. The experiment is performed based on different algorithms for evaluation and comparison.