IoT (internet of things) data is a topic we have discussed a lot in recent years. Traffic data is an important part of IoT data. Traffic flow prediction not only facilitates people’s travel and saves our time, but also provides effective technical support for highway traffic control and scheduling. To achieve accurate traffic flow prediction, this study aims to build a machine learning-based traffic flow prediction model. We first screen out the features that have a greater impact on traffic flow. On this basis, this work establishes a traffic flow prediction model based on CatBoost. By comparing with other machine learning models, conclusions can be drawn: CatBoost model can accurately predict traffic flow; CatBoost outperforms traditional machine learning models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.