Artificial Intelligence in Heat Transfer shows how AI tools and techniques, such as artificial neural networks, machine learning algorithms, genetic algorithms, etc., provide practical benefits specific to thermal sciences. It presents case studies involving heat and mass transfer, multi-objective optimization, conjugate heat transfer, nano-fluids, thermal radiation, heat transfer through porous media (metal foam), and more. Drawing on the collective expertise of leading researchers and experts in multiple fields, the book provides an in-depth understanding of the possibilities that emerge when these tools are applied to problems related to thermal sciences. Artificial Intelligence (AI) is an ever-evolving discipline that has created new and groundbreaking opportunities to advance the mechanical engineering field, particularly in the area of numerical heat transfer. This volume of Advances in Numerical Heat Transfer explores various ways AI is used in heat transfer to solve engineering problems. This book will serve as an important resource for upper-level undergraduate students, researchers, engineers, and professionals, equipping them with the knowledge and inspiration to push the boundaries of the thermal sciences through AI-driven tools and techniques.