Fusion energy research has long captured the public imagination for its applications to fundamental physics, material sciences, and as a low-carbon-footprint electrical power source. The National Ignition Facility (NIF) recently demonstrated that focusing lasers onto a very small target of hydrogen isotopes can produce conditions for nuclear fusion. Despite such remarkable progress, sustainable production of inertial fusion energy (IFE) still presents a huge challenge due to a vast space of parameters that must be explored in order to find optimum conditions for a thermonuclear ignition. It is perceived that artificial intelligence (AI) can play a crucial role in advancing IFE technology. We present our vision of how large language models (LLM) and deep reinforcement learning (DRL) can guide IFE research.