Applied Artificial Intelligence and Intelligent Systems is a comprehensive academic resource designed to bridge foundational AI theory with real-world intelligent system development. The book presents a structured exploration of Artificial Intelligence, beginning with its historical evolution and core principles, and progressing toward modern applications in machine learning, deep learning, and intelligent system design. The content systematically covers essential topics such as intelligent agents and environments, problem formulation, AI paradigms, search strategies, and optimization techniques. It provides in-depth treatment of knowledge representation, probabilistic reasoning, fuzzy logic, and inference mechanisms, enabling readers to understand how intelligent systems reason and make decisions under uncertainty. Dedicated chapters explore machine learning models, deep learning architectures, natural language processing, computer vision, speech recognition, and multimodal systems. The book emphasizes application-driven learning, integrating conceptual clarity with practical insights into system deployment, scalability, and real-time decision-making. Special attention is given to human–AI interaction, explainable AI, ethical considerations, and responsible AI development, ensuring a balanced and future-oriented perspective. With structured explanations, case-oriented discussions, and clear conceptual frameworks, this book serves as a valuable resource for undergraduate and postgraduate students, researchers, and professionals seeking a strong foundation in applied AI and intelligent system technologies.