Abstract: In recent years, with the increasing global demand for food security and the stricter requirements for ecological protection, traditional agricultural and forestry production methods have been unable to meet the development needs of high efficiency, precision and sustainability. Smart agriculture and forestry have become inevitable development directions. Intelligent unmanned aerial vehicle (UAV) technology, with its advantages of high efficiency, flexibility and strong adaptability, integrates modern information technologies such as artificial intelligence, multi-source remote sensing and edge computing, effectively breaking through the limitations of traditional operation modes, and has become a key force in promoting the intelligent and refined transformation and upgrading of agriculture and forestry. This article systematically reviews the research and application progress of key technologies of intelligent UAV in three core application fields: pest and disease monitoring, precise spraying, and forest fire prevention and control. In the field of pest and disease monitoring, multi-source remote sensing information fusion technology enables multi-dimensional information acquisition of crops. Combined with intelligent recognition and classification technology based on deep learning, it provides support for the early diagnosis and dynamic monitoring of pests and diseases. Precision spraying technology accurately extracts crop growth information through the inversion of crop structure parameters and realizes on-demand precise operation relying on variable spraying control technology, which helps to efficiently utilize resources and protect the environment. In forest fire prevention, technologies based on 3D mapping and digital twins—such as pre-fire warning, fire spread simulation, and post-fire assessment—enhance holistic management capabilities throughout the fire cycle. At present, intelligent UAV technology still faces challenges such as insufficient accuracy in extracting fine features, limited generalization ability of models, and the need to improve the stability of real-time processing and system response. In the future, this technology will develop in the direction of deep integration and standardization of multi-source heterogeneous data, research and development of highly generalized and lightweight general models, and cluster collaborative intelligent operations. This article systematically reviews the research progress of related technologies, existing challenges and development trends, providing a reference for promoting the high-quality development of smart agriculture and forestry.