  The ongoing COVID-19 global pandemic is affecting every facet of human lives
(e.g., public health, education, economy, transportation, and the environment).
This novel pandemic and citywide implemented lockdown measures are affecting
virus transmission, people's travel patterns, and air quality. Many studies
have been conducted to predict the COVID-19 diffusion, assess the impacts of
the pandemic on human mobility and air quality, and assess the impacts of
lockdown measures on viral spread with a range of Machine Learning (ML)
techniques. This review study aims to analyze results from past research to
understand the interactions among the COVID-19 pandemic, lockdown measures,
human mobility, and air quality. The critical review of prior studies indicates
that urban form, people's socioeconomic and physical conditions, social
cohesion, and social distancing measures significantly affect human mobility
and COVID-19 transmission. during the COVID-19 pandemic, many people are
inclined to use private transportation for necessary travel purposes to
mitigate coronavirus-related health problems. This review study also noticed
that COVID-19 related lockdown measures significantly improve air quality by
reducing the concentration of air pollutants, which in turn improves the
COVID-19 situation by reducing respiratory-related sickness and deaths of
people. It is argued that ML is a powerful, effective, and robust analytic
paradigm to handle complex and wicked problems such as a global pandemic. This
study also discusses policy implications, which will be helpful for
policymakers to take prompt actions to moderate the severity of the pandemic
and improve urban environments by adopting data-driven analytic methods.
