This study explores the application of artificial intelligence-based teaching supervision systems in vocational education, addressing challenges in traditional teaching and supervision. The system leverages real-time monitoring, behavior recognition, and data analysis to enhance teaching quality and management efficiency. A case study demonstrates significant improvements in student engagement, discipline, and personalized learning outcomes, with classroom interaction rates increasing by 25% and discipline issues decreasing by 40%. Despite challenges in accuracy, data storage, and ethical concerns, the integration of advanced technologies like virtual reality and blockchain offers promising potential for intelligent, data-driven educational models and quality improvement.