Unmanned Aerial Vehicles (UAVs) have witnessed rapid development over the past two decades, driven by significant advancements in electronics, communication systems, and computing technologies. As the complexity of tasks assigned to these platforms continues to increase, there is a growing demand for more intelligent and flexible control systems capable of adapting to dynamic conditions and unpredictable environments. In this context, artificial intelligence has emerged as a fundamental pillar in the development of modern UAV control systems, offering advanced capabilities that surpass the limitations of traditional model-based approaches. Conventional control systems in aerial platforms rely heavily on accurate mathematical models that describe the vehicle’s dynamics. However, such models are often limited in handling external disturbances such as wind variations, sensor noise, and time-varying system uncertainties. Artificial intelligence, particularly machine learning and deep learning techniques, provides a powerful alternative by enabling systems to learn directly from data and adapt to varying conditions without requiring complete model redesign. One of the most prominent applications of artificial intelligence in control systems is the use of artificial neural networks for state estimation and control enhancement. These networks can be trained using flight data to capture complex nonlinear behaviors, thereby providing accurate estimations even in the presence of noise or incomplete measurements. In addition, reinforcement learning algorithms are increasingly being used to develop autonomous control strategies, where the UAV learns optimal decision-making policies through continuous interaction with its environment, progressively improving its performance over time. In practical implementations, artificial intelligence has been effectively integrated into autonomous navigation systems. UAVs can independently plan their trajectories and avoid obstacles using computer vision and image processing techniques. These systems rely on sensor fusion, combining data from cameras, LiDAR, and Inertial Measurement Units (IMUs), which are then processed through deep learning models to generate real-time, reliable decisions. This integration significantly enhances the UAV’s ability to operate in complex environments such as urban areas or challenging terrains. Moreover, artificial intelligence contributes to improving flight stability through the development of adaptive control systems capable of adjusting control parameters in real time. For instance, neural networks can be employed to compensate for system nonlinearities and uncertainties, leading to improved response accuracy and reduced tracking error. This results in more robust and efficient flight performance under varying operational conditions. From a practical perspective, experimental studies have demonstrated that integrating artificial intelligence into UAV control systems leads to reduced energy consumption, enhanced flight efficiency, and improved system reliability. Furthermore, it enables advanced applications such as cooperative swarm operations, where multiple UAVs coordinate their actions using distributed artificial intelligence algorithms, thereby increasing mission effectiveness in both civilian and military domains. Despite these advantages, several challenges remain in implementing artificial intelligence within UAV control systems. These include the requirement for large datasets for training, ensuring the safety and reliability of AI-driven decisions, and computational constraints associated with embedded systems. Therefore, ongoing research focuses on developing more computationally efficient algorithms as well as robust verification and validation techniques to guarantee safe operation within critical boundaries.