Stubble burning is an agricultural post-harvest management practice of combusting crop residues followed by the use of the soil in the fields for the next cropping period. This technique is mostly deployed in Punjab, Haryana, and Uttar Pradesh and is the main factor for unrestricted air pollution, therefore, rural areas experience emissions with particulate matter (PM2.5 and PM10) and greenhouse gases from the burning of the residues. In fact, current strategies that are presently in place such as subsidies for the replacement of the decaying stubble and awareness campaigns have to be improved because of the logistic and economic challenges of implementation. AI's new developments over the last few years offer a realistic approach to dealing with this problem. An example would be the use of artificial intelligence in agriculture and the environment where it helps in crop yield prediction and air quality forecasting. This can further be used as a precursor for predictive decision-making. An example, the CNNs employed by Existing researchers helped in investigating the difference between the stubble burning relationship and the Air Quality Index (AQI) in Indian cities. This paper in addition to the key findings, proposes machine learning algorithms drawn from satellite imagery data, geographic information, and AI techniques (Gaikwad et al. In Modeling Earth Systems and Environment 10:927–941, 2023; Kumari et al. In: International Journal of Computer Vision and Image Processing 4:1–16, 2014).