The imperative for energy efficiency in the built environment has driven significant research into smart building technologies. However, many existing buildings suffer from performance deficits because they were designed without the aid of modern, integrated analysis tools. This paper presents a practical investigation into advancing smart building management by applying integrated Artificial Intelligence (AI) solutions to assess an existing educational building with known environmental comfort issues. The study employs a comprehensive sustainability analysis framework, integrating physics-based simulations of daylighting, wind comfort, microclimate, and noise. Utilizing a detailed 3D model of the building and its context, this research leverages a cloud-based computational platform to conduct these complex analyses efficiently. The platform’s AI algorithms—specifically surrogate models trained for pattern recognition and predictive modeling—are applied to the resulting datasets to identify tangible energy optimization opportunities and inform potential retrofitting interventions. The findings demonstrate a clear pathway for data-driven enhancement, showing how integrating advanced simulation with AI provides actionable insights for creating more sustainable, energy-efficient, and genuinely comfortable educational facilities. This research underscores a paradigm shift from reactive problem-solving to proactive, performance-driven design and retrofitting.