The integration of Artificial Intelligence (AI) in Building Management Systems (BMS) has appeared as an essential approach to intensifying energy efficiency and endorsing environmental sustainability. As buildings account for a substantial portion of global energy feeding and carbon emissions, the integration of AI-driven solutions presents a hopeful path for enhancing resource utilization and plummeting environmental impression. The chapter explores the fundamental gears of AI-driven building management systems, including sensor networks, data analytics, machine learning algorithms, and automated control mechanisms and lessening of carbon emission by forecasting and adjusting energy usage patterns, automating climate control, and managing lighting. It scrutinizes how these systems leverage real-time data to make intelligent decisions about heating, ventilation, air conditioning (HVAC), lighting, and other building operations. The chapter also discusses the role of AI in predictive maintenance, enabling the early detection of faults and reducing energy wastage, predictive maintenance, and overall cost savings. Additionally, the chapter addresses challenges in deployment, ethical considerations, and future directions for research and development in this field, data privacy, system integration, and the high initial cost of implementation. By showcasing real-world case studies and emerging technologies, this chapter illustrates the potential of AI-driven building management systems to contribute significantly to global sustainability goals and the creation of smarter, more efficient urban environments.