AI-Enabled Digital Twins: Soft Computing Methods for Industrial Data Mining

Digital twins (DTs) have become a critical constituent in the continuous digital evolution of industrial systems, enabling instantaneous insight, anticipatory analytics, and tactical enhancement across various sectors. This paper provides an extensive examination of DT principles, historical progressions, market trends, enabling technologies, fields of application, economic impacts, modeling methodologies, challenges, and prospective developments. Special emphasis is placed on the food industry, which exemplifies how DTs can enhance traceability, ensure quality assurance, and build resilience in complex global supply chains. Based on a wide-ranging review of the scholarly literature and industrial analysis, this work highlights how DTs integrate IoT data, AI-driven models, cloud/edge computing, and robust engineering data management systems into cohesive decision-support frameworks. The result is a powerful skill that stimulates continuous improvement, goals toward sustainability, and strategic visioning in dynamic industrial settings.