The rapid expansion of urbanization and the continuously increasing demand for energy have ushered energy governance in smart cities into a new phase—one shaped by the emergence of digital twins and artificial intelligence. However, technological advancement in these two domains alone does not guarantee environmental sustainability. One of the major gaps in the existing literature is the absence of a framework capable of translating the data generated by these technologies into accountable and purposeful decisions. This study aimed to design and validate a conceptual model for smart energy governance based on digital twins and artificial intelligence. The research was conducted in four phases: a systematic literature review based on the PRISMA protocol; thematic analysis of the resulting textual corpus using Braun and Clarke’s six-phase approach; two rounds of the Delphi method involving a panel of eighteen academic and executive experts; and final validation of the model by the same panel. The resulting model comprises three layers: a data-driven layer (digital twin), an analytics-driven layer (artificial intelligence), and an institutional layer (governance), which are interconnected through three mechanisms: a progressive data-to-decision flow, an institutional feedback loop, and a transversal cybersecurity protection layer. Kendall’s coefficient of concordance was 0.74, indicating strong agreement, and the panel confirmed the model’s contribution to enhancing environmental sustainability, with 94.1% agreement. The proposed model demonstrates that integrating these technologies with institutional mechanisms can facilitate data-driven decision-making and enhance energy efficiency in smart cities.