AI-driven energy management systems (AIEMS) are transforming smart buildings by optimizing energy consumption, enhancing operational efficiency, and advancing sustainability. These systems leverage advanced algorithms, machine learning, and data analytics to monitor, analyze, and optimize energy usage. Key components include sensing and data acquisition, data preprocessing, machine learning and optimization algorithms, and control and automation systems. Sensing devices collect real-time data on energy consumption, environmental conditions, and occupancy, while data preprocessing ensures data quality and consistency. Machine learning algorithms predict energy demand, identify patterns, and optimize control strategies, while control systems adjust building parameters in real-time based on predictive analytics. AIEMS offer functionalities such as predictive analytics for energy demand forecasting, adaptive control of HVAC systems, dynamic lighting management, and integration with renewable energy sources. These systems enable demand response, reducing energy consumption during peak times and improving grid stability. They also enhance occupant comfort and reduce environmental impact by optimizing energy use and reducing reliance on fossil fuels. Despite their benefits, AIEMS face challenges such as data privacy concerns, system integration complexity, scalability issues, and user acceptance. Mitigation strategies include robust cybersecurity measures, adoption of open standards, modular architectures, and stakeholder engagement. Future developments include advancements in AI and machine learning, integration with IoT and edge computing, autonomous systems, digital twins, and user-centric design. These innovations will further enhance energy efficiency, sustainability, and resilience in smart buildings. AIEMS are poised to play a crucial role in creating smarter, more sustainable built environments by enabling data-driven decisions and proactive energy management.AI-driven energy management systems (AIEMS) are transforming smart buildings by optimizing energy consumption, enhancing operational efficiency, and advancing sustainability. These systems leverage advanced algorithms, machine learning, and data analytics to monitor, analyze, and optimize energy usage. Key components include sensing and data acquisition, data preprocessing, machine learning and optimization algorithms, and control and automation systems. Sensing devices collect real-time data on energy consumption, environmental conditions, and occupancy, while data preprocessing ensures data quality and consistency. Machine learning algorithms predict energy demand, identify patterns, and optimize control strategies, while control systems adjust building parameters in real-time based on predictive analytics. AIEMS offer functionalities such as predictive analytics for energy demand forecasting, adaptive control of HVAC systems, dynamic lighting management, and integration with renewable energy sources. These systems enable demand response, reducing energy consumption during peak times and improving grid stability. They also enhance occupant comfort and reduce environmental impact by optimizing energy use and reducing reliance on fossil fuels. Despite their benefits, AIEMS face challenges such as data privacy concerns, system integration complexity, scalability issues, and user acceptance. Mitigation strategies include robust cybersecurity measures, adoption of open standards, modular architectures, and stakeholder engagement. Future developments include advancements in AI and machine learning, integration with IoT and edge computing, autonomous systems, digital twins, and user-centric design. These innovations will further enhance energy efficiency, sustainability, and resilience in smart buildings. AIEMS are poised to play a crucial role in creating smarter, more sustainable built environments by enabling data-driven decisions and proactive energy management.