The application of artificial intelligence (AI) in the quality supervision of weaponry and equipment has introduced transformative advancements to the management and maintenance of military assets. This paper systematically examines the potential application scenarios of AI technologies, including predictive maintenance, automated detection, real-time monitoring and data analysis, and quality evaluation. It investigates the implementation processes, key algorithms, practical case studies, and the effectiveness of these technologies in ensuring the safety and reliability of equipment. Additionally, this study discusses the multifaceted challenges AI faces in quality supervision applications, such as data privacy, model interpretability, environmental adaptability, and deployment costs. To address these issues, the paper proposes solutions including technological innovation, regulatory frameworks, and interdisciplinary collaboration to enhance the effectiveness of AI systems in the military domain. This research aims to provide a comprehensive reference for advancing the integration of AI technologies in weaponry management while offering scientific foundations for quality supervision and risk management.