As the global technological landscape evolves, the demand for engineering graduates who possess interdisciplinary skills, critical thinking, and the ability to solve complex, real-world problems is growing exponentially. Artificial Intelligence (AI) presents a powerful opportunity to revolutionize engineering education, enabling the development of these essential competencies in new and transformative ways. This chapter explores how AI can be integrated into engineering education through a comprehensive AI-Driven Engineering Education Integration Model. By embedding AI across key aspects such as curriculum design, teaching methodologies, practical applications, simulations, and research, universities can create dynamic, adaptive, and future-focused learning environments. The chapter also examines how AI enhances graduation projects and research-based learning, offering students the tools and experiences needed to tackle real-world challenges. Emerging concepts like Learning Factories—AI-powered, hands-on learning environments—are highlighted, showcasing a new philosophy for education that emphasizes adaptability and practical experience. AI Twin technology, which simulates real-world engineering scenarios, further enhances experiential learning by providing students with realistic, industry-aligned challenges. Additionally, AI is transforming physical learning spaces, fostering smart, collaborative environments that support both individual and team-based learning. As interdisciplinary and joint degree programs continue to grow, AI is facilitating cross-domain collaboration, allowing students to develop expertise across multiple fields. The chapter also discusses the increasing importance of AI-Driven Professional Engineering Certifications, bridging the gap between academia and industry. Furthermore, the chapter presents a framework for incorporating AI into each stage of the Conceive, Design, Implement, and Operate (CDIO) pedagogy. This framework empowers students and faculty to innovate and collaborate in new ways, using AI-driven tools, simulations, and adaptive learning systems to engage with real-world engineering challenges. Finally, the chapter addresses the infrastructure required for successful AI integration, including curriculum development, faculty training, student engagement strategies, facilities, and ethical considerations.