Under the guidance of the "14th Five-Year Plan" and the "Dual Carbon" goals, construction materials face significant challenges, particularly as the adaptability and accuracy of traditional concrete performance prediction models are questioned. Recently, machine learning (ML) has demonstrated high accuracy and efficiency in predicting concrete performance. The research progress of ML in this field was systematically reviewed, focusing on its applications in mechanical properties, mix design, and durability, while identifying its limitations and proposing improvement strategies. CiteSpace software was used to analyze the current state of ML research in construction engineering, examining publication volume, research hotspots, and trends. This analysis offers valuable reference for future researchers, aiding in the effective application of ML technology to drive innovation in construction materials and support environmental sustainability goals.