Global climate change poses severe threats to agricultural and forested ecosystems that underpin terrestrial carbon balance, biodiversity, and food security. This paper presents a comprehensive investigation into how Artificial Intelligence (AI)—encompassing machine learning, convolutional neural networks (CNNs), long short-term memory (LSTM) networks, transformers, and generative adversarial networks (GANs)—is transforming climate change responses across agriculture and forestry. Drawing on peer-reviewed literature and documented case studies, we examine AI applications including precision irrigation, crop disease detection, yield forecasting, satellite-based deforestation monitoring, wildfire risk prediction, acoustic biodiversity surveillance, and hydrological flood modeling. A three-tiered analytical framework maps causal pathways from technological deployment to environmental, economic, and social outcomes, while critically addressing structural barriers including data scarcity, algorithmic bias, computational inequity, and governance deficits. Principal findings confirm that AI delivers measurable gains in climate mitigation and adaptation efficiency; however, transformative societal potential remains contingent on equitable data access, open-source computational infrastructure, and coherent multilateral policy frameworks.
Dewangan et al. (Thu,) studied this question.