<![CDATA[Objective: This study synthesizes research on the integration of Artificial Intelligence (AI) in physics education within the framework of Education for Sustainable Development (ESD). It aims to map current trends, highlight educational opportunities, and identify research gaps regarding AI’s potential to enhance learning outcomes and foster sustainability competencies. Method: A Systematic Literature Review (SLR) was conducted following PRISMA 2020 guidelines. A total of 48 peer-reviewed studies published between 2015 and 2025 were collected from major academic databases and Google Scholar using Boolean search strings combining terms related to AI, physics education, and ESD. The data were analyzed thematically to identify recurring patterns in AI technologies, physics content areas, ESD dimensions, methodologies, and educational outcomes. Results: The findings indicate that machine learning, deep learning, intelligent tutoring systems, and AI-powered virtual laboratories are the most common applications in physics education. These technologies were primarily applied in mechanics, electricity, and energy-related topics, with limited studies focusing on environmental physics. While AI consistently improved motivation, achievement, and critical thinking, the integration of broader ESD competencies remained uneven, with environmental literacy, social responsibility, and ethical reasoning less frequently addressed. Novelty: This study contributes by linking AI, physics education, and ESD, which are often studied separately, and proposes a conceptual roadmap to align AI integration with sustainable education goals.]]>
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