Sustainable Future through AI-Enhanced Climate Feature Modeling in Healthcare, Agriculture, and Biodiversity

The accelerating impacts of climate change threaten human health, agricultural productivity, and biodiversity stability. This research presents an AI-enhanced framework that leverages satellite-derived climate data to evaluate risks across these critical domains. The approach integrates Kernel Adversarial Gaussian Linear Regression (KAGLR) for detecting climate features associated with health outcomes and a Convolutional U-net Extreme Attention Neural Network (CU-netEANN) for extracting and analyzing environmental patterns. By combining advanced machine learning techniques with large-scale climate datasets, the framework improves prediction accuracy and facilitates early risk detection. The findings highlight the potential of AI-driven climate feature modeling to deliver data-driven insights, promoting sustainable healthcare systems, resilient agriculture, and long-term biodiversity conservation.