With the widespread promotion of smart mobility, there has been increasing attention on the application of vehicular ad hoc network (VANET) in data collection. However, due to the high-speed movement of vehicles and the unpredictability of their trajectories, traditional position-based greedy forwarding strategies struggle to meet the data transmission demands of highly dynamic VANET. To address this issue, an intelligent routing algorithm driven by historical traffic data for VANET (HTD-IR) is proposed. First, an optimal forwarding table for path selection is obtained through an offline learning method based on historical traffic flow information. Then, using an online V2V transmission mechanism based on Markov prediction, the next reliable vehicle is selected according to the vehicle’s motion state. Finally, this study compares HTD-IR with other routing protocols in simulations. The results demonstrate that HTD-IR outperforms in terms of packet delivery ratio, average end-to-end delay, network yield, average successful packet transmission cost, and online computation time complexity.