Sustainable Engineering and Management Practices Enabled by Green AI Technologies

Abstract

The paper examines how Green AI combines with sustainable engineering approaches to management systems. AI stands as a revolutionary approach which improves ecological resource patterns while reducing environmental consequences across economic sectors because society demands sustainable efficient solutions. The research evaluated artificial intelligence techniques Support Vector Machine (SVM), Artificial Neural Networks (ANN), Decision Trees (DT) and Genetic Algorithms (GA) to study their specific applications toward sustainable development in the fields of agriculture, construction and urban planning. The analysis indicates AI-based solutions achieve better performance than traditional ones regarding energy efficiency and waste reduction as well as decision optimization. The predictive capability of SVM improved overall energy consumption by 25% but the resource utilization of ANN reached 20% better than existing models. AI integration into blockchain and IoT systems led to increased sustainability advantages that result in better operational performance in addition to improved environmental sustainability. The research illustrates how Green AI extends its reach toward enabling sustainable development together with fostering environmentally conscious practices for diverse academic disciplines. The urgent need for comprehensive research regarding AI applications for developing efficient resource-based and environmentally beneficial systems emerges as crucial.