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Ai-Driven Data Analytics for Sustainable Resource Management Leveraging Artificial Intelligence to Optimize Energy, Water, Agricultural and Waste Systems

Year: 2026

Type: article

Abstract: The increasing strain on the Earth's limited natural resources, propelled by population expansion, urban development, and climate change, necessitates more intelligent methods for monitoring, predicting, and distributing resources. Artificial Intelligence (AI) and data analytics have surfaced as revolutionary instruments for sustainable resource management, facilitating real-time observation, predictive upkeep, and enhanced decision-making in the energy, water, agricultural, and waste industries. This study analyses fundamental AI methodologies used in resource management, reviews industry-specific implementations and measurable results, and assesses the obstacles hindering widespread adoption. Data gathered from contemporary business and scholarly sources suggests that AI-powered analytics may diminish resource waste by 15-30% and enhance forecasting precision by more than 20%. The document culminates with suggestions for governments and practitioners aiming to expand AI-driven sustainability efforts judiciously.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.23791868

Authors: Natesan, J Pruthvi, Vishwanathan, R, Kumar, S Pradeep, Vigneshkumar, K

Language: English

Topic: Internet of Things and AI

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDGs: Responsible consumption and production, Climate action, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7224919915

FWCI:

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Related to: 10