Vegetation is a central indicator of rangeland condition, yet monitoring it across vast and heterogeneous dryland landscapes remains a major challenge. Although the advantages of remote sensing and unmanned aerial vehicles (UAVs) have been recognized for many years, their evolving role in rangeland vegetation assessment warrants a fresh examination, particularly in light of recent advances in sensor design, data processing, and multiscale integration. This systematic review, conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, synthesizes contemporary developments in UAV-based monitoring of rangeland vegetation. Key progress includes improved flight configurations, enhanced photogrammetric reconstruction for structural mapping, and the increased use of machine learning for estimating vegetation cover and above ground biomass. Emerging tools such as real-time kinematic positioning, automated image processing, and cloud-based computing are accelerating the transition toward transparent, repeatable, and scalable workflows. The integration of UAV products with satellite observations further strengthens regional vegetation assessment and supports broader ecosystem monitoring frameworks. Together, these advances highlight the growing capacity of UAV-based methods to deliver consistent, high-resolution vegetation information for sustainable rangeland management in Australia and globally.