Improving building water efficiency is crucial in meeting the long-term water needs of urban dwellers and achieving global sustainability targets. Although a body of research efforts has been conducted on modeling urban water use, there is still a limited understanding of the impacts of various factors on water consumption in different geographic regions, and of the interdependencies of building water and energy usage. Towards addressing these knowledge gaps, this paper proposes a machine learning-based model to predict the water consumption of buildings based on their physical characteristics and energy consumption levels. Building water consumption data from New York City, Boston, and Philadelphia were used. A support vector regression (SVR) algorithm was used to build the prediction model. The paper discusses the proposed model and its performance results, identifies the features that affect building water consumption and their importance patterns, and analyzes the impacts of the identified factors on water consumption in different cities.