American Journal of Computational and Interdisciplinary Research

E-ISSN: 3069-7093

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

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Volume 2 Issue 6
June 2026

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Interdisciplinary Applications of Machine Learning in Climate Change Prediction and Sustainable Development

Author(s) Shailesh Kumar Pandey
Country India
Abstract Climate change is one of the greatest existential threats facing humanity, with wide-ranging consequences on ecosystems, food systems, energy resources, and socio-economic stability. Traditional climate models, while useful, often fall short in capturing the complexity and interdependence of global systems. Machine Learning (ML), a branch of Artificial Intelligence (AI), has emerged as a transformative technology capable of processing massive datasets, discovering nonlinear relationships, and generating actionable insights. This paper provides an in-depth exploration of the interdisciplinary applications of ML in climate change prediction and sustainable development. It examines its role in predicting extreme weather events, optimizing renewable energy systems, enhancing sustainable agriculture, and advancing urban planning for climate resilience. Furthermore, the paper evaluates the integration of ML with social sciences and policy-making to bridge knowledge gaps and improve decision-making processes. While opportunities abound, ethical concerns, data inequalities, and accessibility challenges must be addressed to ensure equitable and impactful application of ML across developed and developing regions.
Keywords Machine Learning, Climate Change, Artificial Intelligence, Predictive Analytics, Sustainable Development, Renewable Energy, Agriculture, Urban Planning, Climate Policy
Field Engineering
Published In Volume 1, Issue 1, August 2025
Published On 2025-08-05
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