Developing Sustainable Environment Against Air Pollution Impact Using AI

Air pollution remains one of the most pressing global challenges, with multi-faceted consequences for human health, ecosystems, and cultural heritage. This review analyzes various aspects which is affecting the air quality and also explores the necessity for air quality prediction and delves deep into decoding multiple advanced Machine Learning (ML) and Deep Learning (DL) algorithms. The appraising has been performed on gases which predominantly contributes to the change in air quality. The article mainly focuses on the key pollutants such as NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and Particulate Matter (PM) which triggers more environmental damage and health risks. The utilization of Machine Learning (ML) Models and Deep Learning (DL) Models can be elevated by techniques namely data cleaning, feature engineering and hyperparameter tuning to presage air quality. Thereby, this paper highlights the importance of knowing the impact of air quality and steps to safeguard the environment from air pollution. The review aims to foster a cleaner, healthier, and more resilient future for communities worldwide, with a focus on long term sustainability and environmental stewardship.