THE ROLE OF MACHINE LEARNING IN PREDICTIVE ANALYTICS FOR CLIMATE CHANGE AND ENVIRONMENTAL SUSTAINABILITY

Abstract

Climate change poses one of the most pressing challenges of the 21st century, demanding innovative strategies for prediction, mitigation, and sustainable development. Traditional methods of climate modeling and environmental monitoring, while effective, often struggle to capture the nonlinear complexities of environmental systems. Machine Learning (ML), with its capacity to analyze vast datasets and uncover hidden patterns, offers transformative potential in predictive analytics for climate change and environmental sustainability. This article explores the role of ML across key domains, including climate modeling, renewable energy forecasting, disaster prediction, and sustainable resource management. It highlights how supervised, unsupervised, and deep learning methods contribute to improved accuracy in weather forecasting, carbon emission modeling, and ecosystem conservation. Graphical data is presented to illustrate trends in ML adoption, prediction accuracy, and contributions to policy development. The discussion critically evaluates both the opportunities and limitations of ML-based approaches, emphasizing ethical considerations, computational challenges, and the need for interdisciplinary collaboration. The findings suggest that ML not only enhances predictive capacity but also strengthens decision-making frameworks, contributing to global sustainability goals.