High-density orchard systems have become increasingly prevalent in modern fruit production due to their potential for maximizing yield per unit area, yet they present significant challenges in maintaining optimal canopy architecture through precise and timely pruning interventions. Conventional pruning practices rely heavily on operator expertise and subjective visual assessment, leading to inconsistent canopy management, suboptimal light distribution, and inefficient labor utilization. This review examines the emerging integration of augmented reality (AR) technologies as decision-support tools for precision pruning in high-density orchards, focusing on system architecture, sensor integration, and human-machine interaction paradigms. AR-assisted pruning systems combine real-time canopy sensing, three-dimensional plant reconstruction, computer vision algorithms, and spatially registered visual overlays to guide operators through optimized cutting decisions at the individual branch level. Key technological components include wearable and handheld AR display devices, depth-sensing cameras, inertial measurement units, and intelligent pruning recommendation engines based on physiological models and historical yield data. Field deployment studies demonstrate potential improvements in canopy uniformity, light interception efficiency, and operator training acceleration, though challenges remain in environmental robustness, computational performance, and system scalability. This article synthesizes current AR hardware platforms, algorithmic frameworks, and field validation results while identifying critical research gaps and pathways toward commercial adoption in precision orchard management.