This paper applies Machine Learning (ML) to predict the quality of Air-to-Ground (A2G) links performance for Unmanned Aerial Vehicles Base Stations (UAV-BSs) services. UAV-BSs can instantly identify the status of the current 3D wireless channel in an unknown environment without relying on previous statistical channel modeling. The proposed method that employs the unsupervised learning clustering technology applying to A2G channel modeling in 3D wireless communication scenarios. As environment changing, the proposed method can derive the 3D temporary channel model based on collected RSS data and analyzing. To evaluate the proposed method, the simulation data and measurement data are used to co-verify the performance. As the results shown, the RMSE of conventional statistical channel model and proposed temporary channel model are very similar. The similarity achieves about 91.8% both of the simulation and experimental environments to verify the accuracy and feasibility of our proposed method, and that provides more fast and effective of 3D channel modeling approach.