The increasing global demand for energy, coupled with environmental degradation and finite natural resources, has accelerated the need for sustainable and intelligent energy management systems. This study conducts a bibliometric analysis to map the intellectual structure, publication trends, and thematic evolution of research integrating artificial intelligence (AI) and energy sustainability. Using data from Scopus, the analysis identifies energy efficiency, sustainability, renewable energy, artificial intelligence, and machine learning as dominant and wellestablished themes. In contrast, emerging topics such as digital twins, deep reinforcement learning, cyber-physical systems, and multi-objective optimization remain underexplored yet hold significant potential for future research. The findings provide a comprehensive overview of the field's development, highlight influential authors, journals, and affiliations, and propose future research directions for scholars seeking to advance energy optimization through AI-enabled technologies.