The integration of smart technologies with sustainable building practices is becoming increasingly important for reducing urban energy consumption and supporting climate-resilient cities. This study explores opportunities for improving building energy performance within the smart city frameworks of two mid-sized cities with different levels of digital development: Seville, Spain, and Urmia, Iran. A comparative multi-criteria assessment is conducted based on building energy performance, digital governance, renewable energy integration, thermal regulation strategies, and policy frameworks to evaluate the readiness of both cities for intelligent energy management. To validate this assessment, the study presents a conceptual AI- and 5G-IoT-enabled framework for building energy optimization, which integrates real-time sensors, cloud computing, machine learning, and optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Model Predictive Control (MPC). The framework features simulation-based assessment through dynamic energy modeling of buildings using quantitative performance parameters such as Energy Use Intensity, energy savings, reduction of CO₂ emissions, percentage of renewable energy generation, and thermal comfort. The findings indicate that Seville enjoys advanced digital infrastructure, right regulations, and sufficient smart technologies, allowing it to adaptively manage building energy. Meanwhile, Urmia still struggles with infrastructural and organizational challenges, although it has considerable opportunities to enhance energy performance with selective digital investments, passive design approaches, and intelligent control systems. Thus, the proposed framework opens a feasible way for implementing new digital technologies in urban energy management and brings transferable lessons to the cities interested in low-carbon growth through green digital transformation, improved energy efficiency of buildings, and data-driven decision-making.