Foundations of Al and ML: A Unified Approach to Intelligent Systems provides a comprehensive and structured introduction to the essential principles, methods, and applications of Artificial Intelligence and Machine Learning. Designed for undergraduate students, early researchers, and professionals entering the field, this book aims to build a unified understanding of intelligent systems through a balanced treatment of both AI and ML perspectives. The book is organised into six chapters, beginning with a foundational overview of Al and ML, followed by key topics such as problem-solving, search strategies, learning paradigms, algorithmic models, and knowledge representation. Later chapters explore advanced topics including deep learning, convolutional and recurrent neural networks, transfer learning, and ethical considerations surrounding intelligent technologies. What sets this book apart is its clear emphasis on unification, demonstrating the overlaps, distinctions, and synergies between Al and ML. Real-world applications, conceptual clarity, and practical examples are integrated throughout, enabling readers to not only understand the theory but also appreciate its significance in real-life scenarios. Each chapter is carefully crafted with learning objectives, explanatory content, and relevance to modern technological challenges. The text fosters both critical thinking and hands-on exploration, making it ideal for academic coursework and professional upskilling. Whether used in the classroom or for independent study, Foundations of Al and ML: A Unified Approach to Intelligent Systems serves as a timely and accessible guide to the evolving landscape of intelligent system development.