The research on computational intelligence-based smart city architectural planning and landscape design highlights a significant advancement in urban development strategies. By deploying sophisticated algorithms like Improved Ant Colony Optimization (IACO), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), and traditional Ant Colony Optimization (ACO), the study delves into optimizing urban layouts and enhancing the aesthetic and functional aspects of city landscapes. Empirical evaluations of these algorithms demonstrated that IACO, in particular, exhibits superior performance in handling complex, large-scale urban challenges, evident from its high convergence rates and overall efficiency. A key aspect of this research was the integration of resident preferences and behaviors into the urban planning process, ensuring that the design of urban spaces aligns with the needs and desires of city dwellers. Despite the success in various metrics such as safety, convenience, and functionality, the study also identified areas for improvement, particularly in increasing participation and user engagement. This research paves the way for future urban planning endeavors, emphasizing the need to further refine these algorithms to create more inclusive, adaptable, and responsive urban environments, thereby fostering the development of sustainable and intelligent urban landscapes.