In the realm of sustainable architecture, the integration of technology and design has become a transformative force. Building facades significantly impact a structure's energy efficiency, environmental footprint, and aesthetic appeal. This thesis explores the intersection of artificial intelligence (AI) and sustainable design principles, presenting a novel approach to recognize building facades' primary material through semantic segmentation's precision. Selecting appropriate facade materials is crucial in the face of escalating climate concerns and the imperative to curtail carbon emissions. Architects, engineers, and urban planners must embrace data-driven solutions that mitigate the environmental impacts of built environments. During an examination of assessment databases across multiple US cities, it was noted that a significant amount of data is available on building addresses throughout the urban landscape.