Advancing Environmental Sustainability: The Role of Artificial Intelligence in Monitoring, Predicting, and Controlling Sulfur Dioxide Emissions

Air pollution from sulfur dioxide (SO 2 ) emissions poses significant threats to environmental sustainability, public health, and climate change efforts. Despite advances in monitoring and regulatory frameworks, traditional methods often fall short in accuracy, scalability, and efficiency. This study explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in environmental monitoring, SO 2 emission prediction, and desulfurization. It utilizes a literature review of research from 2013 to 2025, assessing AI and ML models for air pollution monitoring, SO 2 emission forecasting, catalytic desulfurization, and SO 2 solubility in ionic liquids. Key findings reveal that AI‐driven models improve predictive accuracy and real‐time monitoring, optimizing desulfurization processes, enhancing air quality, and supporting environmental sustainability. The study highlights the importance of hybrid AI models, improved data collection, and energy‐efficient practices to overcome field limitations. It underscores AI's transformative role in mitigating air pollution, controlling SO 2 emissions, and supporting the achievement of the Sustainable Development Goals.