Advancements in precision agriculture have driven the development of using drones to apply herbicides, offering the potential to address challenges associated with current application methods and improve weed management. This review synthesizes current research on spray drones to develop broad-use recommendations and identify challenges and knowledge gaps. Although spray drones cannot carry a heavy herbicide load compared with ground-based (high-volume backpack or tractor-mounted) sprayers, studies have reported comparable or superior weed control in addition to herbicide cost savings by using drones. However, spray drone performance is highly sensitive to operational parameters, because spray distribution and coverage/deposition are strongly affected by flight height and speed, carrier volume, nozzle design, crop growth stage, weed, and weather conditions. The bell-shaped curve of a single-pass spray pattern, which results in most spray deposition occurring directly under the drone, coupled with advances in imaging, remote sensing, and machine learning, demonstrate the strong potential of using drones for site-specific weed management purposes. Vegetation indices, multispectral imagery, canopy height models, and Light Detection And Ranging (LiDAR) technology have enabled crop-weed discrimination, though accuracy varies with species, growth stage, and image resolution. Deep-learning models such as You Only Look Once (YOLO), Residual Neural Network (ResNet), and Mask Region-based Convolutional Neural Network (Mask R-CNN) are good at weed detection, they remain limited by the quality of training data and their accuracy is reduced when they must detect small, overlapping, or dense weed populations. Spray drone–based offline mapping has enabled substantial herbicide savings by delineating weed patches, whereas real-time weed detection is constrained by onboard processing limits, battery life, and lower spatial resolution at operational flight heights. Ground-based smart sprayers offer higher real-time detection precision but they lack the field accessibility advantages of spray drones. Despite their potential, spray drones face challenges that include limited payload, off-target movement of herbicides, short battery life, regulatory challenges, and extensive license and complex software and calibration requirements. The downwind spray drift potential from drone applications is greater than ground applications but smaller than it is from piloted vehicles. An upwind swath offset is an ideal best management practice for reducing off-target herbicide movement to susceptible areas from both piloted and drone equipment. Future research should evaluate spray drones within integrated weed management systems, focusing on preemergence and foliar-applied contact herbicides, adjuvant use, and environmental and operational interactions to develop spray drone–specific guidelines and optimize spray performance.