China has the largest number of patients with type 2 diabetes (T2D) worldwide, and the chronic complications and economic burden associated with T2D are becoming increasingly severe. Developing accurate and widely applicable risk prediction models is of great significance for the early identification of and intervention in high-risk populations. However, current Chinese models still have many shortcomings in terms of methodological design and clinical application. This study conducts a systematic review and narrative synthesis of existing risk prediction models for type 2 diabetes in China, aiming to identify issues with existing models and provide references with which Chinese scholars can develop higher-quality risk prediction models. This study followed the PRISMA guidelines to conduct a systematic search of the literature related to T2D risk prediction models in China published in English journals from October 2019 to October 2024. The databases included PubMed, CNKI and Web of Science. Included studies had to meet criteria such as clear modeling objectives, detailed model development and validation processes, and a focus on non-diabetic populations in China. A total of 20 studies were ultimately selected and comprehensively analyzed based on model type, variable selection, validation methods, and performance metrics. The 20 included studies employed various modeling methods, including statistical and machine learning approaches. The AUC values of the models ranged from 0.728 to 0.977, indicating overall good predictive capability. However, only one study conducted external validation, and 45% (9/20) of the studies binned continuous variables, which may have reduced the models' generalization ability and predictive performance. Additionally, most models did not include key variables such as lifestyle, socioeconomic factors, and cultural background, resulting in limited data representativeness and adaptability. Chinese T2DM risk prediction models remain in the developmental stage, with issues such as insufficient validation, inconsistent variable handling, and incomplete coverage of key influencing factors. Future research should focus on strengthening multicenter external validation, standardizing modeling processes, and incorporating multidimensional social and behavioral variables to enhance the clinical utility and cross-population applicability of these models. Registration ID: CRD420251072143.