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Particulate matter with a diameter of 2.5 µm (PM 2.5 ) is a critical component of air pollution that has significant impacts on air quality, human health, and the environment. This scientific chapter focuses on the development of a machine learning-based model for predicting PM 2.5 concentrations using the Modern-era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data. The development of this type of model for the prediction of PM 2.5 concentration is crucial for understanding air quality management and mitigating its health impacts. Thus, this chapter aims to create a predictive model utilizing the MERRA-2 dataset, which offers a comprehensive air quality monitoring system and forecasts the air quality index. The chapter also discusses how the model influences advanced machine learning algorithms to predict PM 2.5 levels based on various environmental and meteorological factors (temperature, humidity, wind speed, and atmospheric pressure), along with data preprocessing and model optimization. In addition, to construct the predictive model, several machine learning algorithms, including Random Forest, are also explored in this chapter. The model's performance is assessed using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R 2 ) and can also be discussed with a few results or case studies to provide robust and reliable forecasts for PM 2.5 concentrations. The findings of this chapter highlight the effectiveness of machine learning in predicting air quality and demonstrate the potential of the MERRA-2 dataset as a valuable resource for real-time air quality monitoring and environmental decision-making. This chapter reviews detailed PM 2.5 predictions from satellite aerosol optical depth (AOD), other satellite datasets, and machine learning models. The application of air quality forecast using machine learning and remote sensing reanalysis datasets for environmental engineering provides crucial data for air quality monitoring and managing environmental resources, detecting pollution, assessing the impacts of human activities, and helping engineers and policymakers to make decisions to protect and preserve the air quality and environment. These applications are crucial in enhancing sustainability, improving public health, and ensuring the effective management of environmental resources.