Generative AI is a groundbreaking form of artificial intelligence that swiftly creates contest in response to prompts. The book offers a comprehensive and academically structured introduction to Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs). It presents the fundamental concepts, mathematical intuition, and architectural principles behind modern generative AI technologies in a clear and systematic manner. The book covers key topics such as transformers, large language models, prompt engineering, retrieval augmented generation, generative adversarial networks, diffusion models, multimodal AI, vision language models, and synthetic data generation. Rather than focusing on heavy programming or complex implementation, the book emphasizes conceptual understanding, theoretical depth, and system-level thinking, making advanced AI topics accessible to learners and researchers. Through well-organized chapters and structured explanations, the book helps readers understand how generative models learn, generate content, and solve real-world problems across text, image, and multimodal domains. It also discusses evaluation techniques, ethical considerations, bias, and the future directions of generative AI, enabling students to develop a responsible and research-oriented perspective toward AI technologies. Overall, the book builds a strong foundation in the principles and architectures of generative AI systems.