The rapid growth of urban populations and energy consumption, coupled with the limitations of fossil fuel resources and environmental crises, has highlighted the necessity of managing energy use in cities. Urban energy simulation using semantic 3D models, due to cost reduction, time efficiency, and the ability to evaluate diverse scenarios, is an effective tool for predicting and optimizing energy consumption in existing urban fabrics as well as designing new neighborhoods and cities. This study reviews urban simulation research over the past decade in estimating energy demand. The findings indicate that semantic 3D models enable precise calculation of energy demand, consumption simulation, analysis of renewable energy potential, and assessment of building renovation scenarios. Optimal use of roof space, simplification of building components, and integration of spatial, energy, and climate data enhance simulation accuracy while reducing computation time. Considering the climatic diversity of Iran, it was found that energy simulation parameters must be adjusted according to each climate: in hot-dry and hot-humid regions, the focus should be on cooling load management and solar energy utilization; in cold and mountainous climates, reducing heating loads and improving building insulation are essential; and in temperate-humid climates, combined consumption patterns and humidity and heating–cooling control must be accounted for. Accordingly, developing semantic 3D city models, establishing building energy databases, generating urban energy maps, and performing dynamic energy simulations can serve as effective tools for energy management, efficiency enhancement, sustainable planning, and supporting strategic decision-making in Iran’s architecture and urban planning sectors.