This paper provides an in-depth analysis of current research in UAV swarm formation control. It reviews both traditional methods, including leader-follower, virtual structure, behavior-based, consensus-based, and artificial potential field (APF) approaches, and advanced AI-based methods such as artificial neural networks (ANN) and deep reinforcement learning (DRL). The paper highlights the strengths and limitations of each approach, showing that traditional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization. The review emphasizes the need for innovative solutions and interdisciplinary approaches combining traditional and AI methods to overcome existing challenges and fully exploit the potential of UAV swarms in various applications. UAV swarms offer advantages over single UAVs in terms of efficiency, resilience, scalability, and cost. Formation control involves coordinating UAVs in a precise geometric arrangement or pattern. The paper discusses different formation control schemes: centralized, decentralized, and distributed. Centralized control has simplicity but faces scalability and communication challenges. Decentralized control offers scalability and flexibility but has coordination and design complexity. Distributed control provides collaboration and shared decision-making but requires more communication infrastructure. Traditional methods include leader-follower, virtual structure, behavior-based, consensus-based, and APF methods. Each has its own advantages and limitations. For example, leader-follower is simple but has leader dependency. Virtual structure allows flexible formation but relies on communication. Behavior-based methods are scalable but face coordination challenges. Consensus-based methods ensure convergence but require communication. APF methods are simple but prone to local minima. AI-based methods, such as ANN and DRL, offer data-driven approaches with adaptability and robustness. ANNs like radial basis function, Chebyshev, recurrent, and convolutional neural networks are used for control and prediction. DRL provides adaptive decision-making and robustness to failures. The paper discusses the potential of AI methods in enhancing UAV swarm formation control, while also highlighting challenges such as computational complexity and the need for extensive training data. The review concludes that combining traditional and AI methods can overcome existing challenges and improve the effectiveness, flexibility, and scalability of UAV swarms. Future research should focus on integrating advanced perception, decision-making, and communication technologies to enhance autonomy, adaptability, and robustness.This paper provides an in-depth analysis of current research in UAV swarm formation control. It reviews both traditional methods, including leader-follower, virtual structure, behavior-based, consensus-based, and artificial potential field (APF) approaches, and advanced AI-based methods such as artificial neural networks (ANN) and deep reinforcement learning (DRL). The paper highlights the strengths and limitations of each approach, showing that traditional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization. The review emphasizes the need for innovative solutions and interdisciplinary approaches combining traditional and AI methods to overcome existing challenges and fully exploit the potential of UAV swarms in various applications. UAV swarms offer advantages over single UAVs in terms of efficiency, resilience, scalability, and cost. Formation control involves coordinating UAVs in a precise geometric arrangement or pattern. The paper discusses different formation control schemes: centralized, decentralized, and distributed. Centralized control has simplicity but faces scalability and communication challenges. Decentralized control offers scalability and flexibility but has coordination and design complexity. Distributed control provides collaboration and shared decision-making but requires more communication infrastructure. Traditional methods include leader-follower, virtual structure, behavior-based, consensus-based, and APF methods. Each has its own advantages and limitations. For example, leader-follower is simple but has leader dependency. Virtual structure allows flexible formation but relies on communication. Behavior-based methods are scalable but face coordination challenges. Consensus-based methods ensure convergence but require communication. APF methods are simple but prone to local minima. AI-based methods, such as ANN and DRL, offer data-driven approaches with adaptability and robustness. ANNs like radial basis function, Chebyshev, recurrent, and convolutional neural networks are used for control and prediction. DRL provides adaptive decision-making and robustness to failures. The paper discusses the potential of AI methods in enhancing UAV swarm formation control, while also highlighting challenges such as computational complexity and the need for extensive training data. The review concludes that combining traditional and AI methods can overcome existing challenges and improve the effectiveness, flexibility, and scalability of UAV swarms. Future research should focus on integrating advanced perception, decision-making, and communication technologies to enhance autonomy, adaptability, and robustness.