AI-Driven Innovation for a Sustainable Future: Transforming Electric Machines Engineering

Abstract

The transition toward sustainable and energy-efficient technologies is imperative in the face of escalating environmental challenges. This study explores the integration of Artificial Intelligence (AI) into electric machines engineering as a strategic innovation for achieving sustainability goals. By employing a mixed-method approach, including simulation modeling, statistical analysis, and AI algorithm deployment, the research evaluates the impact of AI-driven systems on performance metrics such as efficiency, carbon emissions, thermal stability, and operational reliability. Results indicate that AI-integrated machines demonstrate up to 6.2% improvement in efficiency, 20% reduction in carbon emissions, and over 22% decrease in downtime across key sectors like electric vehicles, industrial automation, and renewable energy. Advanced AI models—such as LSTM and reinforcement learning—exhibited superior predictive maintenance accuracy, while Principal Component Analysis identified energy efficiency and fault prediction as dominant factors in sustainable performance. These findings highlight AI's potential to transform electric machine systems into intelligent, adaptive, and environmentally responsible technologies. The study offers strategic insights for engineers, manufacturers, and policymakers aiming to harness AI for sustainable industrial innovation.