As the world’s major energy consuming region, the energy transitions of East Asian countries’ energy transitions are crucial to global climate governance. However, studies about these transitions often ignore the nonlinear effects of their drivers, leading to biased simulations of energy transition trends. To address this issue, in this chapter we use machine learning models and interpretable methods to fit global energy transition data from 1990 to 2019, compare the feature relevance and nonlinear effects of energy transition drivers between East Asia and other countries the world over, and simulate their energy transition trends based on the Monte Carlo and stochastic process methods. The empirical results reveal that by region, the energy transition drivers are significantly heterogeneous, and supply and demand vary largely, fully reflecting the strong impact of different endowment constraints on energy transitions among countries. Furthermore, the future trend simulation results indicate that without additional policy interventions, the energy transitions of East Asian countries will grow no less steadily while lagging behind the global average. Following the existing energy transition policies will be insufficient to raise the energy transition level of East Asia to the global average, much less achieve the region’s long-term energy transition goals. The East Asian governments must formulate differentiated energy transition policies based on their respective social conditions and energy resources to achieve a cleaner energy mix and further contribute to global climate governance.