In the realm of building sustainability, the challenge of effectively utilizing building data to drive eco-conscious choices remains a pressing concern. This involves not just gathering comprehensive data about energy use and other environmental indicators but also interpreting this data in meaningful ways. This research aims to address this challenge by introducing an innovative approach based on deep generative models. This approach utilizes water and electricity data as an example of a dataset we can gather to generate data-driven visual representations. The primary aim of this study is to inspire building occupants to embrace sustainable lifestyles and eco-friendly choices by presenting data in an artistic format. Second, to leverage data inference to gather building data to determine building occupancy patterns in various sections. Initial steps involve collecting data on water and electricity consumption, followed by a meticulous data inference process to construct a descriptive analysis that unravels fundamental energy consumption characteristics. Concurrently, a deep neural network is trained to infer occupancy. The sustainability classifier uses features like technology adoption and energy source utilization to categorize data into classes of sustainability. Subsequently, a pre-trained large language model associates specific keywords with each sustainability category. In the final transformation, stable diffusion is utilized to craft the data into visually compelling images to metamorphose the data into visually captivating images. To evaluate the effectiveness of this approach, self-reported data on sustainable behaviors will be gathered through surveys and questionnaires, shedding light on occupants’ awareness and commitment to sustainability. Additionally, monitoring energy consumption data will serve as a tangible indicator of resource usage patterns, revealing how sustainably occupants are utilizing resources.