The accelerating integration of artificial intelligence (AI) into sustainable energy policy presents both transformative opportunities and complex challenges for achieving equity and innovation in the global energy transition. While AI-driven frameworks promise enhanced efficiency, predictive analytics, and optimized resource allocation across renewable energy systems, prevailing policy approaches often overlook the underlying boundary judgments, stakeholder inclusivity, and socio-technical complexities inherent in these transitions. This study applies Critical Systems Heuristics (CSH) to systematically examine the assumptions, values, and power dynamics embedded within AI-assisted policy making for sustainable energy. Employing a qualitative research design that combines a systematic literature review with expert interviews from policy, technology, and community sectors, the analysis utilizes the twelve CSH boundary questions to contrast current “is” framings with normative “ought” expectations for just and innovative energy governance. Findings reveal that dominant AI-enabled policies frequently prioritize technical optimization and economic growth, with limited mechanisms for participatory decision-making, transparency, or recognition of marginalized communities. In contrast, stakeholders advocate for policy frameworks that explicitly address distributive and procedural justice, foster adaptive learning, ensure algorithmic transparency, and embed equity as a core design principle. The study proposes a CSH-informed conceptual framework to bridge these gaps highlighting pathways for inclusive stakeholder engagement, ethical AI deployment, and systemic innovation in sustainable energy governance. The results underscore the necessity of integrating critical systems thinking with digital innovation to advance socially legitimate and resilient energy transitions.