Artificial Intelligence (AI) has become a transformative force in optimizing enterprise infrastructure by enabling intelligent automation, predictive analytics, and real-time decision-making. This study examines the role of AI in enhancing the performance, efficiency, and reliability of enterprise IT infrastructure, including data centers, cloud environments, networks, and storage systems. It explores how machine learning algorithms and advanced analytics are used to monitor system performance, predict failures, optimize resource allocation, and automate routine operations. The integration of AI with cloud computing, edge computing, and Internet of Things (IoT) technologies is analyzed to highlight its impact on dynamic workload management and infrastructure scalability. The paper also discusses the adoption of AIOps (Artificial Intelligence for IT Operations) as a key approach for improving system observability, anomaly detection, and incident response. Key challenges such as data quality, model accuracy, security risks, and implementation complexity are critically evaluated, along with potential solutions and best practices. The findings suggest that AI-driven infrastructure optimization significantly reduces operational costs, enhances system resilience, and supports proactive management strategies, making it a vital component of modern enterprise IT ecosystems.