The safety of overhead transmission lines is threatened by icing. Due to the different degrees of damage caused by ice types including glaze, mixed rime, rime, and wet snow to overhead transmission lines, the de-icing measures taken are also different. When the icing of overhead transmission lines reaches a certain level, appropriate operation and maintenance decisions will be made according to different icing type, including melting the ice, using mechanical removal and adjusting the operation mode. Icing type prediction can provide insights into future icing risks. On the basis of study on identifying insulator icing types through images, a data-driven ice type prediction model is proposed, which fuses monitoring images with micrometeorological data from the past three days. Based on the icing monitoring data of China Southern Power Grid from 2014 to 2021, the nearest micrometeorological monitoring time from the same terminal is searched according to the image capture time. Micrometeorological time series from this time and the past three days is combined with the image to form a sample, constructing a fused dataset of images and micrometeorological data for the data-driven ice type prediction model. The Grid Search Method-eXtreme Gradient Boosting （GSM-XGBoost） is used as the model algorithm, and the micrometeorological time series at 6h intervals from the past three days are used as inputs. The icing types identified from images are used as outputs. With 4 503 fused samples of the training set and 1 931 fused samples of the test set, the macro precision （P_m）, macro recall （R_m）, and macro F1 score of the data-driven icing type prediction model are 95.0 %, 96.3 %, and 95.6 %, respectively. The icing type prediction for overhead transmission lines is achieved accurately.