In this paper, we propose a complete framework named GAN-CTS which utilizes conditional generative adversarial network (GAN) and reinforcement learning to predict and optimize clock tree synthesis (CTS) outcomes. To precisely characterize different netlists, we leverage transfer learning to extract design features directly from placement images. Based on the proposed framework, we further quantitatively interpret the importance of each CTS input parameter subject to various design objectives. Finally, to prove the generality of our framework, we conduct experiments on the unseen netlists which are not utilized in the training process. Experimental results performed on industrial designs demonstrate that our framework (1) achieves an average prediction error of 3%, (2) improves the commercial tool's auto-generated clock tree by 51.5% in clock power, 18.5% in clock wirelength, 5.3% in the maximum skew, and (3) reaches an F1-score of 0.952 in the classification task of determining successful and failed CTS processes.