Roads are vital to support the transportation of people, goods, and services, among others. To yield their optimal socioeconomic impact, proper maintenance of existing roads is required; however, this is typically underfunded. Since detecting road quality is both labor and capital intensive, information on it is usually scarce, especially in resource-constrained countries. Accordingly, the study examines the feasibility of using satellite imagery and artificial intelligence to develop an efficient and cost-effective way to determine and predict the condition of roads. With this goal, a preliminary algorithm was created and validated using medium-resolution satellite imagery and existing road roughness data from the Philippines. After analysis, it was determined that the algorithm had an accuracy rate up to 75% and can be used for the preliminary identification of poor to bad roads. This provides an alternative for compiling road quality data, especially for areas where conventional methods can be difficult to implement. Nonetheless, additional technical enhancements need to be explored to further increase the algorithm’s prediction accuracy and enhance its robustness.