Artificial intelligence (AI) has the potential to transform power plants, making them more efficient, reducing emissions, and aiding with increased renewable energy integration. Nevertheless, its adoption requires addressing complex challenges, such as outdated data infrastructure, risks of displacing workers, and AI’s environmental impact. According to this framework, data-standardized Internet of Things protocols, energy-efficient artificial intelligence hardware, and a hybrid human-artificial intelligence system are used to maximize predictive maintenance and grid stability. From an environmental perspective, AI-driven strategies such as dynamic combustion control and inertia simulation can counter renewable intermittency while reducing emissions. Socially, transparent data practices promote trust, and reskilling and upskilling in a workforce-based approach to adopting AI systems ensures equitable transitions. As such, co-regulatory policies should be ethically obligated to put the needs of marginalized communities above all else in global deployments, balancing innovation against accountability. What we define as sustainable success depends on a mix of reconciling technical feasibility with circular economy principles, reducing resource consumption for AI and intergenerational equity. To align AI’s potential with a just, low-carbon energy future, a collaborative, multidisciplinary approach—across policymakers, industries, and communities—is critical.