在“十四五”规划和“双碳”目标指引下，建筑材料的组成面临着重大挑战，尤其是传统混凝土性能预测模型的适应性和准确性受到质疑。近年来，机器学习（machine learning, ML）技术在混凝土性能预测领域展示出较高的预测精度和效率。系统回顾了ML在混凝土性能预测方面的研究进展，特别聚焦于其在混凝土的力学性能、配合比设计及耐久性评估等方面的应用进展和不足，并提出相应的改进策略。此外，还利用CiteSpace软件探讨了ML在建筑工程领域的研究现状，从发文量、研究热点及其演进趋势等角度综合分析，不仅为未来研究者提供参考，也旨在助力其更有效地利用这一技术，推动建筑材料的创新发展与环境可持续性目标的实现。

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.