By the end of 2017, kudzu bug was reported in 652 counties in the United States since it was first observed in Georgia in 2009. Modeling its invasion dynamics is valuable to guide management through early detection and prevention of further invasion. Herein, we initially estimated the spread rate of kudzu bug with county-level invasion records and then determined important spatial factors affecting its spread during years 2010–2016. As kudzu bug infests a large heterogeneous area and shows asymmetric spread, we first utilized spatially constrained clustering (SCC), an unsupervised machine learning method, to divide the infested area into eight spatially contiguous and environmentally homogenous neighborhoods. We then used distance regression and boundary displacement methods to estimate the spread rates in all neighborhoods. Finally, we applied multiple regression to determine spatial factors influencing the spread of kudzu bug. The average spread rate reached 76 km/yr by boundary displacement method; however, the rate varied largely among eight neighborhoods (45–144 km/yr). In the southern region of the infested area, host plant density and wind speed were positively associated with the spread rate, whereas mean annual temperature, precipitation in the fall, and elevation had inverse relationships. In the northern region, January minimum temperature, wind speed, and human population density showed positive relationships. This study increases the knowledge on the spread dynamics of kudzu bug. Our research highlights the utility of SCC to determine natural clustering in a large heterogeneous region for better modeling of local spread patterns and determining important factors affecting the invasions.