With the rapid development of artificial intelligence and intelligent vision technologies， images and videos have evolved from media primarily intended for human viewing into essential sources of information for machine perception， understanding， and decision-making. Conventional visual coding methods primarily optimize pixel reconstruction fidelity and perceptual quality for human vision. However， these objectives do not necessarily ensure the efficient preservation of semantic information and discriminative features required by machine vision tasks， such as object detection， semantic segmentation， object tracking， and action recognition. Coding for machine vision has therefore attracted increasing attention from both academia and industry as an important research direction in visual information compression. This paper systematically reviews its key technologies and development trends， covering the optimization of conventional coding standards and deep-learning-based coding methods. First， machine-oriented optimization methods for conventional image and video coding standards are examined， with particular attention to task-aware bit allocation， rate-distortion optimization for machine vision， and coding tool optimization. Second， deep-learning-based methods are organized into three categories according to the coding object： Learned image coding， learned video coding， and learned feature coding. The evolution of these technologies from pixel reconstruction toward semantic preservation， task performance preservation， and edge-cloud collaborative intelligent processing is analyzed. Finally， the paper discusses major challenges concerning evaluation protocols， generalization across tasks and models， computational complexity， compatibility with existing standards， and the joint requirements of human-machine collaboration and privacy protection. Future research directions are also outlined， including unified rate-distortion modeling， general-purpose visual representation coding， lightweight intelligent coding， human-machine collaborative coding， and standardization.Highlights:1.A systematic review of coding for machine vision is presented， covering conventional coding standard optimization and learned image， video， and feature coding.2.Representative methods are compared in terms of core ideas， key techniques， and coding performance， tracing the shift from pixel reconstruction to semantic preservation and task-oriented compression.3.Major challenges and future directions are discussed， including unified rate-distortion modeling， general-purpose visual representation coding， lightweight deployment， human-machine collaborative coding， and standardization.