Intelligent control and automation have become core themes of modern engineering, science, and technology. They provide adaptive, robust, and efficient solutions for complex systems, differing from traditional methods that rely on fixed models and strict rules. Intelligent control combines computational intelligence technologies such as fuzzy logic, neural networks, genetic algorithms, and reinforcement learning to handle uncertainty, nonlinearity, and real-time decision-making. These approaches enable effective operation in dynamic environments, narrowing the gap between theory and practice. The rise of intelligent automation has transformed manufacturing, transportation, energy, and healthcare. Through predictive maintenance, adaptive optimization, and autonomous decision-making, intelligent systems have improved efficiency, safety, and sustainability. In manufacturing, they enable flexible production and smart factories aligned with Industry 4.0. In transportation, they support self-driving cars and traffic management. In the energy sector, they integrate renewable energy and enhance grid stability. In healthcare, they enable advanced robotics and personalized treatment systems. Despite promising prospects, challenges remain. These include computational complexity, limited explainability and trust, vulnerability to uncertainty and cyber threats, and the balance between autonomy and human oversight. Addressing these issues requires advances in algorithm design, interpretability, system security, and interdisciplinary collaboration. This article examines the foundations, methods, and applications of intelligent control and automation. It also discusses current challenges, ethical implications, and future trends. Ultimately, it demonstrates that intelligent control and automation can drive technological progress, reshape industries in an interconnected world, promote sustainable development, and improve human life quality.