Urban engineering is the discipline that studies and designs urban infrastructure as integrated and co-dependent systems. It is by nature inter-disciplinary, including elements of urban planning, urban metabolism, computer science, and civil, electrical, and mechanical engineering. Urban infrastructure systems include buildings, transport, water, wastewater, electricity, gas, solid waste, and telecom infrastructure systems. Thanks to the availability of new data sources and significant development in data science, the discipline has advanced significantly in the past decade. In this seminar, I will talk about urban engineering, describing how urban infrastructure systems are interdependent and I will offer a rationale for planning and designing them together as opposed to independently. I will then briefly introduce the world of Machine Learning (ML) and show recent results where we used XGBoost (an advanced gradient boosting technique) and SHAP (SHapley Additive exPlanation) to study interrelationships between electricity, gas, and water consumption in large scale buildings in New York City. Finally, I will finally offer a four-step urban infrastructure design process that can be applied to any infrastructure project and that focuses on infrastructure integration and decentralization. Overall, there is a real need to study urban infrastructure together, and urban engineering offers a means to do so, with the ultimate goal to design cities that are smart, more sustainable, and more resilient.