Purpose of Review <p>Forest roads are vital for the relocation of wood harvesting machinery, secondary transportation, forest management, fire prevention, and recreational activities. Monitoring and maintaining the condition of a large and spatially distributed road network requires resource-efficient methods and new expertise. This review examines methods for measuring and predicting the condition and trafficability of unpaved forest roads, based on 87 publications from 2010 to 2025. The aim was to examine evaluated road features and the methods used to measure them.</p> Recent Findings <p>Forest road condition is commonly evaluated by structural strength, surface quality, geometry, and drainage. Traditional techniques (e.g., bearing capacity measurements and visual inspections) are the most frequently used. Mobile mapping approaches and remote sensing methods, which include airborne laser scanning, unmanned aerial vehicle surveys, and satellite‑based observations, have been increasingly applied to assess road condition. Machine and deep learning methods have recently been used to identify potholes, rutting, and surface roughness based on image, video, and sensor data. Other approaches employed to predict and evaluate road condition include laboratory testing, traffic monitoring, transportation data, and meteorological records.</p> Summary <p>Although traditional field methods remain dominant, emerging technologies are increasingly used to assess forest road condition. Reliable prediction of trafficability requires integrating multiple data sources. Despite technological advances, real-time dynamic road condition maps and situation awareness are lacking, and determining the actual condition of a road often requires a field visit. Novel digital solutions and data sources could improve condition forecasting and support more efficient forest road network maintenance. However, the generalizability of these findings may be limited by the scope of the review and language constraints.</p>