Academic libraries constitute the epistemic infrastructure of higher education institutions, serving as knowledge repositories and research facilitation environments. The rapid evolution of Artificial Intelligence (AI), particularly in machine learning (ML), natural language processing (NLP), and knowledge graph engineering, is catalyzing a paradigm shift in academic library ecosystems. AI-driven systems enable semantic information retrieval, automated metadata generation, predictive collection development, and intelligent decision support. Through API-based interoperability and large-scale metadata aggregation, AI platforms deliver personalized recommendation systems, automate repetitive cataloguing tasks, optimize search precision, and enhance bibliometric analytics. This study critically evaluates advanced AI tools and frameworks integrated within academic libraries to improve discoverability, research productivity, and scholarly communication. The findings suggest that AI architectures significantly strengthen metadata harmonization, citation intelligence, and dynamic knowledge visualization while raising essential ethical and infrastructural considerations.