<p>Meta-heuristic algorithms are favored for their superior performance in sustainable supply chain management (SSCM) optimization. Despite extensive research in this field, there remains a lack of systematic reviews specifically addressing the application of meta-heuristic algorithms in SSCM optimization in China, as well as a comparative analysis with international research. To address this research gap, the present study employs bibliometric and content analysis methodologies, integrating 41 in the CNKI academic journal database indexed documents with 238 in the Web of Science core collection indexed documents, to systematically synthesize existing research trajectories and construct a theoretical framework. This study systematically compares the performance of major meta-heuristic algorithms and critically analyzes their limitations and challenges in the context of SSCM optimization. The results indicate the following: The number of published Chinese and international literature on this topic is increasing. Genetic algorithm (GA) and non-dominant sorting Genetic Algorithm II (NSGA-II) are the most commonly used meta-heuristic algorithms. According to key words cluster analysis, the primary research topics include sustainability management, environmentally friendly supply chain, supply chain collaboration and integration, meta-heuristic algorithms and related fields. The most commonly used hybrid strategies are NSGA-II and Variable Neighborhood Search (VNS), GA and Particle Swarm Optimization (PSO), with frequencies of 4 and 6 respectively. These algorithms are primarily employed to address practical issues such as supply chain network design, vehicle routing problems, and scheduling problems. The shortcomings of the existing research are discussed in detail, and on this basis, the specific suggestions for future research are discussed.</p>