Artificial Intelligence and Digital Twin Technologies as Drivers of Sustainable Industrial Innovation

Digital transformation has significantly changed industrial production and management processes. This study explores the application of artificial intelligence and digital twin technologies to improve operational efficiency, productivity, and industrial sustainability. A conceptual framework is developed by integrating real-time sensor data, machine learning algorithms, and digital simulation to predict operational conditions and equipment maintenance requirements. The results suggest that AI-enabled digital twin systems can help industries identify potential equipment failures, optimize energy consumption, and reduce material waste. These technologies also support data-driven decision-making by allowing companies to evaluate various production scenarios before implementing them in physical systems. However, system interoperability, data security, and workforce readiness remain important challenges. This study highlights the potential of artificial intelligence and digital twin technologies as strategic tools for developing more adaptive, efficient, and sustainable industries in the future.