  The quality of air is closely linked with the life quality of humans,
plantations, and wildlife. It needs to be monitored and preserved continuously.
Transportations, industries, construction sites, generators, fireworks, and
waste burning have a major percentage in degrading the air quality. These
sources are required to be used in a safe and controlled manner. Using
traditional laboratory analysis or installing bulk and expensive models every
few miles is no longer efficient. Smart devices are needed for collecting and
analyzing air data. The quality of air depends on various factors, including
location, traffic, and time. Recent researches are using machine learning
algorithms, big data technologies, and the Internet of Things to propose a
stable and efficient model for the stated purpose. This review paper focuses on
studying and compiling recent research in this field and emphasizes the Data
sources, Monitoring, and Forecasting models. The main objective of this paper
is to provide the astuteness of the researches happening to improve the various
aspects of air polluting models. Further, it casts light on the various
research issues and challenges also.
