Integrated Sensing and Communications (ISAC) is emerging as a foundational paradigm for next-generation wireless networks, enabling communication infrastructures to simultaneously support data transmission and environment sensing. By tightly coupling radio sensing with communication functions, ISAC unlocks new capabilities for situational awareness, localization, tracking, and network adaptation. At the same time, the increasing scale, heterogeneity, and dynamics of future wireless systems demand self-organizing network intelligence capable of autonomously managing resources, topology, and services. Artificial intelligence (AI), particularly learning-driven and data-centric methods, has become a key enabler for realizing this vision. This survey provides a comprehensive and system-level review of AI-native ISAC-enabled self-organizing wireless networks. We develop a unified taxonomy that spans: (i) ISAC signal models and sensing modalities, (ii) network state abstraction and perception from sensing-aware radio data, (iii) learning-driven self-organization mechanisms for resource allocation, topology control, and mobility management, and (iv) cross-layer architectures integrating sensing, communication, and network intelligence. We further examine emerging learning paradigms, including deep reinforcement learning, graph-based learning, multi-agent coordination, and federated intelligence that enable autonomous adaptation under uncertainty, mobility, and partial observability. Practical considerations such as sensing-communication trade-offs, scalability, latency, reliability, and security are discussed alongside representative evaluation methodologies and performance metrics. Finally, we identify key open challenges and future research directions toward deployable, trustworthy, and scalable AI-native ISAC systems for 6G and beyond.

翻译：集成感知与通信（ISAC）正成为下一代无线网络的基础范式，使通信基础设施能够同时支持数据传输与环境感知。通过将无线感知功能与通信功能紧密耦合，ISAC为态势感知、定位、跟踪和网络自适应等应用开启了新的能力。与此同时，未来无线系统日益增长的规模、异构性和动态性，要求网络具备能够自主管理资源、拓扑和服务的自组织智能。人工智能（AI），特别是学习驱动和数据为中心的方法，已成为实现这一愿景的关键使能技术。本文对支持AI原生ISAC的自组织无线网络进行了全面且系统级的综述。我们构建了一个统一的分类体系，涵盖：（i）ISAC信号模型与感知模态，（ii）基于感知感知无线电数据的网络状态抽象与感知，（iii）面向资源分配、拓扑控制和移动性管理的学习驱动自组织机制，以及（iv）集成感知、通信与网络智能的跨层架构。我们进一步探讨了新兴的学习范式，包括深度强化学习、图学习、多智能体协同和联邦智能，这些范式使得网络能够在不确定性、移动性和部分可观测条件下实现自主适应。本文同时讨论了感知-通信权衡、可扩展性、时延、可靠性和安全性等实际考量，并介绍了代表性的评估方法与性能指标。最后，我们指出了面向6G及更远未来的可部署、可信赖、可扩展的AI原生ISAC系统所面临的关键开放挑战与未来研究方向。