Efficient and accurate road boundary detection is a fundamental building component of the perception system for autonomous driving. Specially, the challenges for road boundary detection in surface mine are high generalization error of model and difficulty in boundary generation, which caused by diversity of samples along with scarcity for corresponding samples and complexity of terrain respectively. Therefore, a novel road boundary detection framework, which execute in a high efficiency way with considerable performance, is proposed for the problems mentioned above. Firstly, point cloud pre-processing methods, including point cloud down-sampling, filtering and clustering, are conducted for achieving clusters of objects in surface mine. Then, a meta random forest classification method, which combines meta learning and random forest for enhancing the generalization ability of the model and overcoming sample scarcity of surface mine, is proposed for classifying point cloud clusters of retaining wall on both side of the road. At last, the boundary of unstructured road is generated by conducting a series of post-processing methods corresponds to the unevenness and irregularity of unstructured road. Experiments are carried out on the collected and labeled datasets of surface mine. The results illustrate that our proposed method can effectively detect road boundary of surface mine in real-time with considerable performance.