Unmanned aerial vehicle （UAV） vision-language navigation stands as a core research direction of aerial embodied intelligence，which combines computer vision （CV），natural language processing，robot control，and unmanned system technology. This system endows UAVs with the ability to understand human natural language instructions and perform autonomous navigation，path planning and task execution in unstructured，GPS-denied and dynamically changing three-dimensional environments. Unlike traditional UAV navigation methods that rely on satellite positioning，inertial navigation or manual waypoint setting，UAV vision-language navigation establishes a direct mapping from high-level semantic instructions to low-level continuous flight actions. This enables UAVs to complete complex tasks，such as target search，obstacle avoidance，long-distance cruising，and scene reasoning only through visual observation and language understanding. This technology effectively breaks the limitations of traditional navigation in semantic interaction and environmental adaptability. At the same time，it has theoretical value and engineering application prospects in low-altitude economies，urban security，emergency rescue，industrial inspection，and precision agriculture. In recent years，with the rapid development of multimodal large models，Transformer architectures and embodied artificial intelligence （AI），UAV vision-language navigation has gradually evolved from early modular pipelines to end-to-end learning frameworks，and then to highly interpretable reasoning systems driven by large language models （LLMs） and vision-language models. However，compared with ground robot vision-language navigation，UAV vision-language navigation still faces many unique challenges caused by aerial movement characteristics. First，UAVs move with six degrees of freedom，and frequent changes in height，pitch，yaw，and roll lead to unstable visual input，large differences in object scales and serious geometric distortion，which in turn increases the difficulty of cross-modal alignment between vision and language. Second，three-dimensional spatial topology is more complex，and spatial prepositions，such as “above”，“between”，“along”，and “across” require stronger geometric reasoning and spatial awareness，which cannot be satisfied by two-dimensional image matching alone. Third，long-range navigation tasks bring certain problems，such as massive visual information，cumulative positioning errors，and semantic forgetting，which put forward higher requirements for efficient memory mechanism and environmental representation. Fourth，the discrete semantic decision space is difficult to match with the continuous physical control spaces of UAVs，resulting in a large gap between simulation training and real-world deployment. With the goal of solving the abovementioned problems，this paper systematically summarizes the research progress of UAV vision-language navigation from the perspective of embodied intelligence. First，three types of simulation platforms are reviewed： general robot simulation platforms，simulation platforms based on real scene reconstruction，and large-scale virtual simulation platforms built by game engines. These platforms provide safe，low-cost，and high-efficiency test environments for algorithm verification，data generation，and model training，as well as effectively narrow the domain gap between simulation and reality. Then，mainstream datasets are compared and analyzed from the dimensions of scene scale，instruction complexity，action space definition，and environmental authenticity. The development of datasets reflects the trend of UAV vision-language navigation moving from indoor structured scenes to outdoor large-scale urban scenes，from short and simple to long sequential reasoning instructions，and from discrete actions to continuous six-degree-of-freedom control. Next，the core technical framework is divided into four modules： perception representation，reasoning paradigm，memory storage and embodied control. The perception representation focuses on visual feature extraction，geometric alignment，multimodal fusion，and world model construction. The reasoning paradigm mainly includes cross-modal attention mechanism，transformer-based pretraining，and large-model-driven chain-of-thought reasoning. The memory storage evolves from implicit temporal memory to explicit semantic maps and topological graphs. Furthermore，the embodied control develops from modular trajectory planning to end-to-end vision-language-action generation with safety constraints. In terms of practical applications，UAV vision-language navigation has been widely explored in urban infrastructure inspection，disaster emergency search and rescue，intelligent logistics distribution，and precision agricultural monitoring. This can reduce manual participation，improve operation efficiency，and enhance safety under high-risk，high-complexity and high-efficiency-demanding conditions. However，key bottlenecks continue to restrict large-scale deployment，such as simulation-to-real transfer，generalization in unseen environments，real-time performance of onboard computing，safety and stability in dynamic environments，and collaborative navigation of multiple UAVs. Finally，this paper discusses the prospects and future development trends of UAV vision-language navigation，including stronger world model and predictive reasoning，better generalization and robustness based on multimodal large models，safer and more reliable physical control，more efficient human-machine interaction and collaborative intelligence，as well as deeper integration with low-altitude digital economy and unmanned system ecology. Overall，this review aims to provide a complete and clear technical route for researchers in the fields of embodied intelligence，computer vision，natural language processing，and UAV systems； promote the breakthrough of key technologies； and accelerate the practical and industrialization process of UAV vision-language navigation.