What does it mean for a digital twin to be truly next-generation? Arguably, the answer no longer lies in simply mirroring a physical asset but in embedding intelligence, enabling self-adaptation, and integrating across domains as diverse as pharmaceutical manufacturing, urban energy governance, audiology, and engineering education. This book brings together state-of-the-art research on machine learning surrogate models that accelerate real-time decision-making, value-at-risk frameworks for self-adaptive systems under uncertainty, and high-fidelity geospatial twins for sustainable urban management. Readers will also explore human-centered applications, e.g., personalized digital twins for hearing healthcare, alongside foundational perspectives on how to teach microwave engineering for frequency-aware Internet of Things. The book advances a distinctive dual focus: rigorous technical depth, from surface impedance models to 3D visualization in Unreal Engine, alongside a pedagogical commitment to immersive learning environments and training twins. Key advantages of this collection include its cross-sector applicability, bridging industrial automation, healthcare, and smart cities; its emphasis on practical surrogate and hybrid modeling approaches; and its forward-looking treatment of challenges, e.g., underspecification, failure propagation, and learning-to-synchronize. Whether you are an engineer, researcher, educator, or policymaker, this book offers a concise yet panoramic view of where digital twin technology stands today—and, more importantly, where it is heading tomorrow.