Artificial Intelligence for Environmental Sustainability: A Systematic Review of Applications, Opportunities, and Challenges

Abstract

Artificial intelligence (AI) is increasingly recognized as a transformative technology for advancing environmental sustainability through improved monitoring, predictive modeling, resource optimization, and evidence-based environmental decision-making. This study presents a systematic review of the applications, opportunities, and challenges of AI in environmental sustainability, focusing on its contributions to climate change mitigation, biodiversity conservation, pollution control, renewable energy management, water resource protection, and sustainable agriculture. The review synthesizes existing literature on AI-driven technologies, including machine learning, deep learning, computer vision, natural language processing, and remote sensing, to examine their practical applications and environmental implications. The reviewed evidence indicates that AI can enhance the accuracy of environmental monitoring, predict ecological changes, identify pollution sources, optimize energy consumption, and support the efficient management of natural resources. Furthermore, integrating AI with satellite imagery, sensor networks, geographic information systems, and environmental datasets creates opportunities for real-time surveillance, early-warning systems, and more responsive sustainability policies. Despite these benefits, the adoption of AI for environmental sustainability faces significant challenges, including data scarcity and inconsistency, high computational energy consumption, infrastructure costs, algorithmic bias, limited model interpretability, privacy concerns, and inequalities in technological access. The environmental footprint of AI itself also raises important questions regarding the balance between technological benefits and resource consumption. The review therefore emphasizes the importance of developing energy-efficient algorithms, improving data governance, strengthening interdisciplinary collaboration, and establishing transparent regulatory frameworks. It concludes that AI can make a substantial contribution to environmental sustainability when implemented responsibly and integrated with scientific knowledge, inclusive governance, and broader sustainability objectives. Realizing its full potential requires balancing technological innovation with ecological responsibility, social equity, and long-term environmental protection.