The construction of clinical prediction models based on biochemical indicators has become an important direction in medical research. This paper systematically reviews recent advances in the development of clinical prediction models based on biochemical indicators and machine learning techniques with a focus on the key aspects of model construction highlighting the current limitations of such models and proposing future directions for improvement. We enrolled 28 studies that are retrieved from PubMed Embase Web of Science and China Knowledge Network CNKI databases summarizing and analyzing the modeling objectives variable selection modeling methods and model evaluations of these studies. We found that most of the included studies were small-sample single-center designs with a median sample size of 466 n = 54-58 616 . The study populations primarily consisted of cancer patients and the purpose of the studies was mainly prognosis/ risk prediction of disease. The model performance varied widely with AUC values ranging from 0. 691 to 0. 992 and the qualities of enrolled studies varied. Further analysis revealed several limitations including unclear inclusion / exclusion criteria lack of reliable preprocessing methods absence of feature engineering and insufficient cross-validation and external validation. Therefore although a few studies attempted to establish prediction models using biochemical indicators the overall quality of the research still needs improvement. Future research should focus on optimizing models using multivariate variable selection and advanced machine learning / deep learning algorithms adopting standardized evaluation methods for model validation to ensure the clinical applicability of the models and incorporating time-series data to enhance model quality and fully realize the clinical value of biochemical indicators.