AbstractWith the continuous advancement in data-acquisition devices, computer vision techniques, and machine-learning (ML) algorithms over the past decades, artificial intelligence (AI) technology has increasingly been applied to research and practice in pavement engineering and related fields. This paper is aimed at systematically synthesizing the state-of-the-art in applying AI algorithms and techniques to various areas of pavement management. To achieve this goal, the authors reviewed major work published in archival journals from 2015 to 2020. Key findings from the review are synthesized and presented based on three broad categories of pavement management activities: distress evaluation, performance modeling, and maintenance and rehabilitation (M&R) programming. Most of the reviewed studies have achieved positive and/or promising results, proving the effectiveness of leveraging AI algorithms for pavement management. Distress detection and classification are found to be the areas that attracted the most attention in terms of applying AI techniques and algorithms. In contrast, applying AI techniques to M&R programming represents a major research gap. Based on the review, it can be concluded that AI algorithms have made noticeable achievements in most activity areas of pavement management, although some major research gaps remain to be filled.