Textbook in PDF format Artificial Intelligence in Heat Transfer shows how Artificial Intelligence (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, nanofluids, 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. 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, Advances in Numerical Heat Transfer, explores various ways AI is used in heat transfer to solve engineering problems. The chapters in this book provide a detailed examination of various methodologies, from Machine Learning applications to AI-enhanced simulations, making this an essential resource for researchers, engineers, and industry professionals. Chapter 1 introduces the role of AI in heat transfer and fluid dynamics, highlighting its applications in aerodynamics, chemical processing, and power generation. Chapter 2 delves into machine learning techniques for fluid mechanics, addressing flow prediction, turbulence modeling, and optimization. Chapter 3 explores AI-driven computational fluid dynamics (CFD) to enhance simulation accuracy and reduce computation time. Chapter 4 investigates the application of artificial neural networks in analyzing natural convection in hybrid nanofluids. Chapter 5 presents an AI-based optimization framework for heat transfer in gyrotactic-nanofluid flow. Chapter 6 discusses AI-driven techniques for optimizing heat exchanger design and performance. Chapter 7 focuses on AI-powered energy optimization in HVAC systems using machine learning and game theory. Chapter 8 applies artificial neural networks to radiative heat transfer in magnetized stenosed arteries. Chapter 9 examines AI-driven flow optimization techniques in renewable energy systems. Chapter 10 expands on AI’s role in enhancing efficiency and reliability in renewable energy grids. Chapter 11 investigates deep learning approaches for optimizing energy management in sustainable power systems. Chapter 12 applies physics-informed neural networks to model exothermic reactions in porous media. Chapter 13 compares artificial neural networks with traditional numerical methods for solving MHD hyperbolic tangent nanofluid flow. Chapter 14 provides case studies showcasing AI applications in industrial thermal and fluid processes. Chapter 15 explores AI-driven advancements in microfluidics and nanofluidics for precise flow control and diagnostics. 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