AI-driven innovations in network and storage optimization: Transforming infrastructure management

Artificial Intelligence is revolutionizing network and storage infrastructure management by enabling intelligent optimization across increasingly complex and distributed environments. This article explores the theoretical foundations and practical applications of AI-driven approaches to infrastructure optimization, examining how machine learning techniques transform traditional management paradigms. The evolution from rule-based systems to sophisticated learning algorithms has enabled dynamic traffic management, predictive maintenance, intelligent resource allocation, and automated performance optimization. Despite demonstrating significant benefits, the integration of AI into infrastructure environments presents substantial challenges related to data quality, security considerations, organizational factors, and standardization requirements. These challenges necessitate innovative solutions that bridge technical and operational domains while ensuring appropriate governance of increasingly autonomous systems. Future directions in this field include edge computing integration, explainable AI development, cross-domain optimization approaches, and enhanced human-AI collaboration frameworks that will shape the next generation of intelligent infrastructure management systems.