Abstract: Cold chain multimodal transport combines the particularity of cold chain transportation and the complexity of multimodal transportation. Compared with general transportation problems, its path optimization models are more complex and impose higher demand on solution algorithms. This paper presents a comprehensive review on the path optimization of cold chain multimodal transportation, focusing on models and algorithms. First, a knowledge graph visual analysis is conducted based on 369 Chinese and English literatures using a systematic literature review method. Then, 40 key literatures among them that address both model construction and algorithm solutions are further examined. The selection of optimization objectives, cost calculation, and model-solving algorithms are summarized. It is found that most single-objective optimization studies focus on minimizing the total transportation cost, while multi-objective studies mainly minimize transportation cost, transportation time, and carbon emissions, maximize customer satisfaction. Although multi-objective optimization better reflects real-world scenarios, it often involves complex models and high computational complexity. Therefore, most studies still adopt single-objective optimization. For cost modeling, including refrigeration, cargo damage, and carbon emissions, most studies commonly use fixed cost coefficients and seldom consider the impact of different transportation conditions. Solution algorithms mainly fall into two categories: exact algorithms and heuristic algorithms. Exact algorithms are suitable for small-scale optimization problems, while machine learning-based heuristics are often combined with other algorithms and demonstrate greater advantages in solving large-scale practical problems. Finally, future development directions are discussed in terms of model construction, objective selection and computation, as well as algorithm implementation.