Abstract: The concrete mix ratio determines its cost, workability, mechanical properties, and durability. The traditional method of concrete mix ratio optimization is through a large number of laboratory tests, which consumes a lot of time, labor, and resources. To solve the above problems, concrete proportion optimization using machine learning and meta-heuristic optimization algorithms has been proven to be a promising technical tool. Presents a comprehensive review of the research on concrete proportion design and optimization. First, the basic working principles and advantages of commonly used machine learning and meta-heuristic algorithms are discussed. Then, the applications of machine learning and meta-heuristic-based algorithms in single-objective and multi-objective optimization of various types of concrete proportions are summarized. Finally, current trends and opportunities in advancing the field of concrete proportion design and optimization are highlighted and discussed in the context of the current state of the art, providing a basis for deeper development and application of machine learning techniques in the field of concrete.