Understanding the spatiotemporal dynamics of seasonal influenza spread across the United States (US) is crucial for informed public health planning. We explored patterns of influenza transmission during the 2022/23 season in the US and used a mathematical model to infer potential drivers and underlying mechanisms. Leveraging emergency department visit data, we first estimated the timing of influenza onset for the 2022/23 season at the Health Service Area (HSA) level. We then combined the estimated onset times in a gravity-based mechanistic model with covariates that could be associated with influenza spread, including demographics, climate, mobility, and school opening information. We compared multiple models to find the best fit to the onset times, infer factors driving transmission, and identify potential geographic hubs that were most influential in generating early chains of transmission. From the estimated onset times, we found a spatiotemporal pattern that was characterized by early transmission in southern and southeastern HSAs, followed by localized spread to other regions. The best-fit model included absolute humidity and local transmission modulated by school opening times, with four out of five potential hubs located in the southern US (two in Georgia and one each in North Carolina and Texas) and one in the Northwest (Washington). In conclusion, we found a regional pattern for the spatiotemporal spread of 2022/23 seasonal influenza and identified potential key drivers of this pattern. These findings, and similar studies from other influenza seasons, may improve our understanding of the spatiotemporal spread of influenza in the US.