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The modern world is characterized by complexity, ambiguity, and uncertainty—elements that traditional binary logic systems are ill-equipped to handle. As systems evolve and interact across diverse domains like artificial intelligence, engineering, medicine, finance, and social sciences, the need for flexible, human-centric, and uncertainty-resilient approaches becomes essential. Fuzzy Logic and Uncertainty Modelling: Foundations, Methods, and Applications emerges from this very need—to provide a comprehensive, structured, and accessible introduction to the field of fuzzy systems and their role in modelling uncertainty in real-world systems. This book is designed for a diverse audience: from undergraduate and postgraduate students to academic researchers and industry professionals. It begins with the theoretical underpinnings of uncertainty—exploring the limitations of classical logic and the emergence of fuzzy logic as proposed by Lotfi Zadeh in 1965. From there, it gradually advances to complex formulations such as type-2 fuzzy sets, fuzzy neural networks, fuzzy genetic algorithms, fuzzy cognitive maps, and hybrid models combining probability, rough sets, and fuzziness. Throughout the book, mathematical rigor is balanced with intuitive explanations. Every chapter is structured to build foundational knowledge first and then apply that understanding through equations, real-world examples, applications, and case studies. Key domains such as image processing, autonomous vehicles, smart grids, medical diagnosis, NLP, and decision support systems are explored in-depth to show how fuzzy logic enables better decisions in ambiguous and dynamic contexts. In the final sections, the book dives into cutting-edge research areas like quantum fuzzy logic, explainable AI (XAI), fuzzy logic in IoT, and ethical implications of fuzzy systems. Practical tools such as Python libraries, benchmark datasets, and open-source frameworks are also discussed to support hands-on implementation. By combining foundational theory, modern applications, and interdisciplinary reach, this book aspires to not only educate but also inspire. Readers will leave with a clear understanding of how to navigate and model uncertainty using fuzzy logic—not just as a theoretical tool, but as a robust framework for solving the world’s most intricate and uncertain problems.