The Role of AI for Renewable Energy Optimization and Smart Resource Management Toward Global Sustainability

The increasing growth of the world energy demand, along with the increasing pace of environmental degradation and climatic risks, has emphasized the necessity to replace the fossil-fuel-based systems by cleaner and more sustainable renewable-energy alternatives. The real-time monitoring, control, and optimization of contemporary energy networks are made possible by the emerging technologies, such as artificial intelligence (AI), digital twins, the Internet of Things (IoT), and smart-grid infrastructures. In this paper, recent developments in the use of AI in renewable-energy systems, smart cities, and sustainable industrial processes are reviewed. According to the earlier researches, light AI models, upgraded IoT systems and integrating renewables (such as solar-biogas-assisted electric-vehicle charging) can help greatly in decreasing emissions, lowering the cost of their operation and reducing the amount of energy loss. Further studies in the agricultural, manufacturing and building industries show that automation driven by AI, digital-twin approaches and resource-optimisation solutions improve the environmental performance. Also green financing systems, sustainable supply-chain policies and conducive energy policies are also key elements in hastening this transformation. In spite of these benefits, there have been some challenges namely, data-security issues, expensive cost of deployment, limitation of scalability as well as lack of stakeholder adoption. All in all, this paper highlights the revolution that smart technologies have in addressing net-zero goals, climate resilience, and sustainable development. It brings together existing information, outlines areas of research needs, and emphasizes the need to have a solid policy, international collaboration, and further innovation to push the next wave of clean-energy revolution.