This paper provides an in-depth analysis of the current research landscape in UAV (Unmanned Aerial Vehicle) swarm formation control. It examines both conventional control methods, such as leader–follower, virtual structure, behavior-based, consensus-based, and artificial potential field (APF), and advanced AI-based methods, including artificial neural networks (ANNs) and deep reinforcement learning (DRL). The review highlights the distinct advantages and limitations of each approach, emphasizing that conventional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization capabilities. The paper underscores the critical need for innovative solutions and interdisciplinary approaches to overcome existing challenges and fully exploit the potential of UAV swarms in various applications. The formation control problem in UAV swarms involves managing and coordinating the movement and behavior of a group of UAVs flying in a specified formation. Key sub-problems include formation representation, trajectory tracking, formation generation, formation keeping, formation switching, collision avoidance, and formation dissolution. The control schemes discussed include centralized, decentralized, and distributed approaches, each with its own advantages and challenges. Conventional strategies are based on specific models and can be categorized into leader–follower, virtual structure, behavior-based, consensus-based, and APF methods. Each method has its strengths and limitations, such as simplicity, flexibility, and adaptability, but also faces challenges like scalability, robustness, and environmental adaptability. AI strategies, particularly ANNs and DRL, have gained prominence due to their effectiveness in solving complex problems. ANNs, especially deep neural networks, are used to handle complex dynamics and autonomous decision-making processes. DRL combines reinforcement learning techniques with deep neural networks to enable agents to handle high-dimensional problems and learn optimal policies through interaction with the environment. The paper concludes by discussing the state of the art in UAV swarm formation control methods, highlighting technological breakthroughs and future directions to overcome existing limitations.This paper provides an in-depth analysis of the current research landscape in UAV (Unmanned Aerial Vehicle) swarm formation control. It examines both conventional control methods, such as leader–follower, virtual structure, behavior-based, consensus-based, and artificial potential field (APF), and advanced AI-based methods, including artificial neural networks (ANNs) and deep reinforcement learning (DRL). The review highlights the distinct advantages and limitations of each approach, emphasizing that conventional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization capabilities. The paper underscores the critical need for innovative solutions and interdisciplinary approaches to overcome existing challenges and fully exploit the potential of UAV swarms in various applications. The formation control problem in UAV swarms involves managing and coordinating the movement and behavior of a group of UAVs flying in a specified formation. Key sub-problems include formation representation, trajectory tracking, formation generation, formation keeping, formation switching, collision avoidance, and formation dissolution. The control schemes discussed include centralized, decentralized, and distributed approaches, each with its own advantages and challenges. Conventional strategies are based on specific models and can be categorized into leader–follower, virtual structure, behavior-based, consensus-based, and APF methods. Each method has its strengths and limitations, such as simplicity, flexibility, and adaptability, but also faces challenges like scalability, robustness, and environmental adaptability. AI strategies, particularly ANNs and DRL, have gained prominence due to their effectiveness in solving complex problems. ANNs, especially deep neural networks, are used to handle complex dynamics and autonomous decision-making processes. DRL combines reinforcement learning techniques with deep neural networks to enable agents to handle high-dimensional problems and learn optimal policies through interaction with the environment. The paper concludes by discussing the state of the art in UAV swarm formation control methods, highlighting technological breakthroughs and future directions to overcome existing limitations.