The convergence of artificial intelligence (AI), Internet of Things (IoT) networks, and advanced heating, ventilation, and air conditioning (HVAC) systems represents a critical frontier in sustainable smart building operations. As urbanization intensifies and energy demands surge, the optimization of building environmental systems has become essential to minimize energy consumption while enhancing indoor air quality (IAQ) and occupant comfort. This research explores the theoretical underpinnings, design considerations, and practical implementations of AI-enabled HVAC systems integrated with distributed IoT monitoring networks, emphasizing the predictive capabilities, energy savings, and reliability improvements achievable in contemporary built environments. Emphasis is placed on the utilization of low-power wide-area network technologies such as NB-IoT for real-time monitoring, AI-driven predictive analytics for energy optimization, and sustainable integration with renewable energy systems. The study synthesizes knowledge from recent research on machine learning frameworks for facility management, the role of low-cost air quality sensors, and the efficacy of intelligent HVAC control algorithms based on predictive indices. Methodologically, the study adopts a descriptive, conceptual synthesis approach, mapping existing technological capabilities onto theoretical energy efficiency models and evaluating potential performance improvements through scenario-based analysis. Results indicate that a synergistic application of AI, IoT, and predictive control mechanisms significantly enhances energy efficiency, reduces carbon footprints, and maintains optimal IAQ parameters across variable occupancy scenarios. Challenges related to sensor drift, unit-to-unit variability, network reliability, and integration with legacy building management systems are analyzed in depth, alongside strategies for mitigation through calibration propagation, data-driven maintenance, and adaptive control protocols. The findings underscore the potential of AI-IoT-HVAC convergence as a transformative paradigm in sustainable building design and management, offering a pathway toward intelligent, resilient, and environmentally responsible urban infrastructures.

AI-enabled HVAC systems, Internet of Things, Indoor air quality, Predictive maintenance

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