Climate change presents a significant global challenge, affecting ecosystems, societies, and
economies (Weiskopf et al., 2020). We require advanced analytical tools for a better
understanding of future scenarios and the upcoming climate conditions. This may include
different predictive models and environmental impact assessment results. Machine learning
(ML) and Artificial Intelligence (AI) have become important technologies in recent years that
improve our comprehension of future climatic scenarios and the environmental effects of
human activity (Zennaro et al., 2021).
Multiple studies have shown that AI and ML techniques are increasingly used in weather and
climate modelling areas (Levy et al., 2024). The AI-driven frameworks have significantly
improved weather forecasting, climate process simulation and flood risk prediction methods.
Moreover, neural networks have refined agricultural predictions and optimised crop yield
studies under different climate scenarios. Depending on the requirements, a machine
learning model can operate using anything from basic linear regression to a sophisticated
deep learning model. We can use AI in different environmental monitoring setups besides
climate modelling studies. Recently, MI techniques have been widely used to understand
deforestation rates, biodiversity loss, and carbon sequestration (Kolevatova et al., 2021).
These studies prove that combining deep learning techniques and different statistical
methods enhances the assessment of deforestation’s impact on carbon emissions.
Even with the notable progress in AI-enhanced climate modelling, a thorough grasp of AI’s
global influence on climate science is still required. New insights into the cause of climate
change, mitigation techniques and sustainable development solutions are made possible by
the ongoing evolution of AI technologies. By incorporating AI and ML into climate sciences,
researchers can improve environmental impact assessments, maximise the development of
sustainability policies and offer innovative solutions to climate challenges. As AI advances, it
is expected to play an increasingly significant role in climate research by providing more
precise and computationally effective tools for weather and extreme events prediction, and
evaluating and mitigating the effects of climate change.
We also employ different ML techniques for data analysis, interpretation of results and
physical systems, coupled climate modelling, and atmospheric chemistry. The important
papers we have published using different Machine learning and deep learning techniques
are listed below.