Abstract
The way we predict service life and model pavement performance gradually evolves due to the popularity of machine learning (ML). We now have better tools to handle complex, multidimensional and big data through applications of recent advancements in deep learning and large language models (LLMs). Thus, the pavement deterioration can be predicted earlier and with greater accuracy because of models’ ability to identify patterns that conventional techniques might miss. In addition to support and continuous monitoring through automated analysis of sensor and visual inputs, ML helps through learning from historical data, traffic patterns, and environmental factors. In order to investigate current trends, we performed a literature analysis after conducting a systematic review in accordance with PRISMA guidelines. Our results state that although ML has gained popularity, the pavement engineering is still in its infancy. In this field, more sophisticated techniques, e.g., generative AI and LLMs have not yet been thoroughly investigated. Even though there are still issues, especially with data quality and model transparency, ML presents great potential in enabling intelligent infrastructure management.