Intelligent systems are vital to modern engineering, enabling autonomy, adaptability, and efficient decision-making. This paper reviews recent advances in learning, estimation, and control—three core pillars of intelligent systems. It covers data-driven and model-based learning methods, including machine learning and reinforcement learning, for decision-making under uncertainty; estimation techniques like distributed and nonparametric methods for dynamic state reconstruction; and robust, optimal, and predictive control strategies for complex, uncertain environments.  The integration of these elements enhances system resilience...