With the rapidly increasing energy demand in the AI ​​era, the need for a future energy source, especially nuclear fusion technology, is growing. For power generation using nuclear fusion energy, ultra-high-temperature hydrogen plasma must be maintained stably for extended periods. However, determining the temperature of this plasma (diagnosis), how it changes (prediction), and how to maintain the plasma at a desired state (control) remain active research topics. This talk will introduce my ongoing research utilizing machine learning techniques for plasma diagnosis, prediction, and control. This will cover data-driven time-series prediction using nuclear fusion data, approaches to inverse problems using physics-informed neural networks, and nuclear fusion control and optimization using reinforcement learning.