Single-frame remote sensing image super-resolution can improve the spatial resolution of imaging without requiring a change in hardware, making it an important research direction in remote sensing. In recent years, owing to their excellent generative capability and training stability, diffusion models have shown great potential in the field of super-resolution remote sensing imaging. Given the rapid development of research in this field, it is necessary to conduct a comprehensive review of existing diffusion model-based super-resolution algorithms for remote sensing to help researchers accurately grasp the technical context and development trends. This paper surveys the application of diffusion models to single-frame SR remote sensing imagery. First, we elaborate on the intrinsic characteristics of remote sensing images from three perspectives—the imaging physics process, semantic features, and data scale and task objectives—thereby laying a tailored foundation for algorithm design. Subsequently, we categorize existing algorithms based on three core technical approaches: model architecture optimization, training paradigm innovation, and prior-knowledge fusion. For a comprehensive and objective evaluation of current algorithms, we compile mainstream datasets and multi-level evaluation metrics applicable to super-resolution remote sensing image tasks. Finally, combining the current technical bottlenecks, we propose four future development directions, aiming to provide theoretical support and technical guidance for the development of diffusion models in the field of super-resolution remote sensing imaging.