Purpose: This report investigates how artificial intelligence (AI) technologies can address the persistent challenge of stubble burning in agricultural regions by enabling real-time detection, forecasting pollution, and promoting residue management alternatives.

Methods: A qualitative synthesis is provided, referencing AI applications deployed by institutions such as ISRO, IBM Weather AI, and Indian Agricultural Research Institute (IARI), with a focus on satellite data analytics, air quality modeling, and farmer decision support tools.

Results: AI-based systems demonstrate a capacity to detect crop fires with over 85% accuracy, predict air quality deterioration up to 72 hours in advance, and reduce burning incidents when coupled with advisory services. Tools like IBM’s Environmental Intelligence Suite and IARI’s PUSA bio-decomposer app showcase applied successes.