Abstract The accelerating effects of climate change, driven primarily by greenhouse gas emissions, have intensified the global need for effective carbon footprint reduction strategies. Traditional mitigation approaches alone are insufficient to address the complexity and scale of modern environmental challenges. Artificial Intelligence (AI) has emerged as a transformative technology capable of enabling intelligent automation, predictive analysis, and large-scale system optimization. This paper presents an in-depth analysis of the role of AI in reducing carbon footprints and supporting sustainable development across multiple sectors, including energy systems, transportation, manufacturing, agriculture, and urban infrastructure. The study examines key AI techniques such as machine learning, optimization algorithms, predictive analytics, and Internet of Things (IoT) integration, highlighting their applications in emission monitoring, energy efficiency, and resource optimization. Challenges related to energy-intensive AI models, data quality, ethical considerations, and policy gaps are also discussed. The paper concludes that AI, when responsibly designed and implemented, can act as a critical enabler in the transition toward a low-carbon, resilient, and sustainable future.
Jadhav et al. (Sat,) studied this question.