Artificial Intelligence (AI) has emerged as a transformative force in the design, enhancement, and deployment of metadata systems within academic libraries. The current review synthesizes insights from recent studies focusing on AI-driven metadata enrichment, interoperability frameworks, cataloguing automation, user experience transformation, and the strategic repositioning of libraries in the digital era. Key contributions across the literature indicate that AI technologies—particularly machine learning, natural language processing, deep learning, and generative AI—are redefining metadata workflows, enabling faster resource discovery, improving interoperability, and supporting large-scale metadata harvesting through protocols such as OAI-PMH. Research further reveals that academic libraries are shifting from passive service units to proactive knowledge facilitation centres as AI augments both operational efficiency and decision-making processes. At the same time, challenges persist in areas such as ethics, metadata bias, algorithmic transparency, sustainability of AI models, and the need for digital competencies among library professionals. This review consolidates evidence from global library environments—including Asia, Europe, Africa, and the United Kingdom—highlighting converging trends and diverging implementations. It also proposes a future-oriented model for AI-enabled metadata ecosystems by integrating the findings of recent scholarly works. This review contributes to understanding how libraries can strategically adopt AI to enhance metadata quality, integrate digital collections, support research, and ensure long-term interoperability.