The rapid integration of artificial intelligence (AI) into interprofessional learning (IPL) within medical education presents novel ethical and governance challenges that require systematic examination. This study aims to explore and synthesize existing ethical frameworks governing AI-enhanced IPL in cross-disciplinary medical education, focusing on identifying governance gaps and emerging trends. Employing a qualitative literature review methodology, this research systematically analyzed 80 peer-reviewed articles sourced via academic databases and managed with Mendeley Desktop. Data were collected through comprehensive document screening and extraction processes, emphasizing relevance, quality, and thematic alignment. Thematic content analysis was applied to identify recurring ethical concerns, governance models, and cross-disciplinary implications. Findings reveal that data privacy, algorithmic bias, transparency, and learner autonomy are central ethical considerations in AI-mediated IPL environments. However, governance mechanisms remain fragmented, with limited inclusion of AI expertise in oversight structures and inconsistent accountability frameworks. The review highlights significant cultural and disciplinary variability affecting ethical implementation and stresses the need for adaptable, context-sensitive governance models. The findings highlight that comprehensive ethical systems involving multiple disciplines are crucial for managing the complexities associated with AI-supported interprofessional education. Further studies should aim to empirically test governance structures and investigate how stakeholder collaboration can promote just and clear integration of AI in the context of medical education.