As urbanization accelerates, optimizing energy efficiency has become a pressing challenge for sustainable city planning. Business Intelligence (BI) tools, powered by AI and predictive analytics, offer data-driven solutions to enhance energy management, reduce consumption, and integrate renewable resources. This study examines the role of AI-driven BI in optimizing urban energy systems through real-time monitoring, predictive forecasting, and decision support mechanisms. A comparative analysis of six global cities—Amsterdam, Singapore, New York, Pune, London, and Tokyo—demonstrates how BI-driven insights contribute to energy savings, grid stability, and carbon footprint reduction. The results indicate that cities leveraging BI tools achieve up to a 30% improvement in energy efficiency and a 45% increase in renewable energy integration, highlighting the transformative potential of AI-enhanced BI systems. However, challenges such as data integration complexities, scalability concerns, and regulatory barriers remain significant. The study also explores emerging trends, including blockchain-enhanced BI for energy security, federated learning for decentralized smart grid optimization, and AI-driven autonomous energy management systems. These insights provide actionable recommendations for policymakers, energy planners, and urban developers aiming to implement BI-driven energy solutions at scale.