Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants—particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)—were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p< 0.05). Due to the lockdown, the mean concentrations decreased for NO2and CO by −4.1 ppb and −0.088 ppm, respectively, while it increased for O3by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by −50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.