Generative AI (GAI) is increasingly being applied to unmanned vehicle (UV) swarms to address challenges in coordination, decision-making, and environmental adaptation. UV swarms, composed of multiple autonomous vehicles, offer advantages such as scalability, flexibility, and resilience in complex environments. However, traditional AI methods face limitations in dynamic and uncertain settings, prompting the need for GAI. This paper provides a comprehensive survey of GAI applications, challenges, and opportunities in UV swarms. UV swarms include Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and Unmanned Underwater Vehicles (UUVs), each designed for specific tasks. GAI techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Generative Diffusion Models (GDMs), Transformers, and Normalizing Flows are explored for their potential in enhancing UV swarm operations. These techniques offer capabilities in data generation, environmental simulation, and decision-making, which are crucial for autonomous navigation, task allocation, and swarm intelligence. Applications of GAI in UV swarms include state estimation, environmental perception, autonomy, and task/resource allocation. For instance, GANs and VAEs are used to generate realistic data for training and simulation, while Transformers and Normalizing Flows improve data processing and decision-making. GAI also enhances environmental perception by improving image resolution and generating synthetic datasets, which are essential for autonomous navigation and task execution. In terms of autonomy, GAI enables more efficient and adaptive decision-making through techniques like Generative Adversarial Imitation Learning (GAIL) and Variational Autoencoders (VAEs). These methods allow UVs to adapt to dynamic environments and perform complex tasks. Additionally, GAI improves task and resource allocation by optimizing routing strategies and managing computational resources in multi-agent systems. The paper highlights open issues and future research directions in GAI for UV swarms, including scalability, adaptive GAI, explainable swarm intelligence, security/privacy, and heterogeneous swarm intelligence. Overall, GAI holds significant potential to enhance the performance and efficiency of UV swarms in various applications, from surveillance and environmental monitoring to healthcare and industrial automation.Generative AI (GAI) is increasingly being applied to unmanned vehicle (UV) swarms to address challenges in coordination, decision-making, and environmental adaptation. UV swarms, composed of multiple autonomous vehicles, offer advantages such as scalability, flexibility, and resilience in complex environments. However, traditional AI methods face limitations in dynamic and uncertain settings, prompting the need for GAI. This paper provides a comprehensive survey of GAI applications, challenges, and opportunities in UV swarms. UV swarms include Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and Unmanned Underwater Vehicles (UUVs), each designed for specific tasks. GAI techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Generative Diffusion Models (GDMs), Transformers, and Normalizing Flows are explored for their potential in enhancing UV swarm operations. These techniques offer capabilities in data generation, environmental simulation, and decision-making, which are crucial for autonomous navigation, task allocation, and swarm intelligence. Applications of GAI in UV swarms include state estimation, environmental perception, autonomy, and task/resource allocation. For instance, GANs and VAEs are used to generate realistic data for training and simulation, while Transformers and Normalizing Flows improve data processing and decision-making. GAI also enhances environmental perception by improving image resolution and generating synthetic datasets, which are essential for autonomous navigation and task execution. In terms of autonomy, GAI enables more efficient and adaptive decision-making through techniques like Generative Adversarial Imitation Learning (GAIL) and Variational Autoencoders (VAEs). These methods allow UVs to adapt to dynamic environments and perform complex tasks. Additionally, GAI improves task and resource allocation by optimizing routing strategies and managing computational resources in multi-agent systems. The paper highlights open issues and future research directions in GAI for UV swarms, including scalability, adaptive GAI, explainable swarm intelligence, security/privacy, and heterogeneous swarm intelligence. Overall, GAI holds significant potential to enhance the performance and efficiency of UV swarms in various applications, from surveillance and environmental monitoring to healthcare and industrial automation.