Clathrate hydrates of natural gases are important backup energy sources. It is thus of great significance to explore the nucleation process of hydrates. Hydrate clusters are building blocks of crystalline hydrates and represent the initial stage of hydrate nucleation. Using dispersion-corrected density functional theory (DFT-D) combined with machine learning, herein, we systematically investigate the evolution of stabilities and nuclear magnetic resonance (NMR) chemical shifts of amorphous precursors from monocage clusters CH4(H2O)n (n = 16-24) to decacage clusters (CH4)10(H2O)n (n = 121-125). Compared with planelike configurations, the close-packed structures formed by the water-cage clusters are energetically favorable. The 512 cages are dominant, and the emerging amorphous precursors may be part of sII hydrates at the initial stage of nucleation. Based on our data set, the possible initial fusion pathways for water-cage clusters are proposed. In addition, the 13C NMR chemical shifts for encapsulated methane molecules also showed regular changes during the fusion of water-cage clusters. Machine learning can reproduce the DFT-D results well, providing a structure-energy-property landscape that could be used to predict the energy and NMR chemical shifts of such multicages with more water molecules. These theoretical results present vital insights into the hydrate nucleation from a unique perspective.