This study analyzed user preferences for green facades in four types of residential buildings: one-story houses, two-story homes, residential apartments, and condominium units. Using fuzzy clustering of k-means, clear patterns emerge showing that apartment buildings and condominiums tend to have more unified user preferences. At the same time, one-storey and two-storey houses exhibit tangible variability. These insights can inform architects and developers in tailoring green facade designs to better align with user expectations for different residential typologies. The application of a fuzzy-k clustering technique in machine learning predicts consumer preferences and supports the development of personalized green facade designs in architectural practice and urban planning. The results of this study can capitalize on the user’s liking for green facades to inform sustainable urban planning, architectural standards, and policy interventions. By aligning green design interventions with public appreciation and functionality expectations, these interventions can be more easily adopted and viable in the urban landscape.