Travel-time delays due to recurring congestion result in productivity loss, likelihood of accidents, and environmental pollution due to greenhouse gas emission. The National Highway Traffic Safety Administration in the United States has listed several driver assistance technologies that are now common in most of the newer vehicles. While these technologies can help reduce the likelihood of traffic related accidents, they can do very little to reduce recurring congestion prevalent in urban areas. Recurring congestion during rush hours is prevalent, for example, along Interstate 95 and Capital Beltway 495 in the Baltimore-Washington area. Such congestion enhances the likelihood of crashes. Previous approaches to hotspot identification are primarily theoretical which limits their practical applicability. This paper develops a Machine Learning approach by way of a geospatial and neural network integration to predict traffic congestion hotspots during rush hour. The approach uses live traffic sensor data. A case study from Maryland is presented. The result shows top hotspot segments across Maryland. Using a snapshot of hotspots at eight different time periods, the likelihood of hotspot locations is predicted using an artificial neural network. The research can serve as a valuable tool for traffic congestion hotspot identification and travel-time prediction.