The accelerating rise in to global warming, melting polar ice, erratic weather patterns, and intensity of natural disasters such as floods, wildfires, and droughts. Conventional statistical tools have struggled to capture the complex, non-linear relationships within vast and multidimensional climate datasets, limiting their effectiveness in prediction and mitigation strategies. To address this issue, the proposed research introduces a machine learning-based framework titled “Global Environment Analysis Using Machine Learning.” The system integrates Support Vector Machines (SVM), K-Nearest Neighbors (KNN), to analyze structured environmental data. The dataset comprises critical climate indicators such as temperature anomalies, carbon emissions, rainfall variability, sea level rise, deforestation rates, and pollution metrics. Preprocessing steps such as normalization, outlier handling, and missing value imputation are employed to enhance data quality. Dimensionality reduction The findings indicate strong correlations between anthropogenic activities and environmental degradation. Visual outputs including geospatial heatmaps and real-time dashboards are designed to present insights in an accessible manner for researchers, policy makers, and environmental agencies. This work demonstrates the potential of intelligent data-driven systems to enable proactive environmental monitoring, predictive risk assessment, and sustainable decision-making. The proposed solution lays the groundwork for future integration into climate informatics platforms, urban planning tools, and environmental conservation programs.
Girish Kumar D (Mon,) studied this question.