This study focuses on the development and evaluation of machine learning models, particularly the Adaptive Boosting (AdaBoost) algorithm, for predicting lymph node metastasis (LNM) in cervical cancer (CC) patients. The findings show that AdaBoost outperformed traditional statistical methods and other machine learning models, including Random Forest, Support Vector Machine (SVM), and Least Absolute Shrinkage and Selection Operator (LASSO) regression, in predicting LNM. The areas under the curve (AUCs) for the training and validation sets were 0.882 and 0.857, respectively, indicating high prediction efficiency. Multivariate logistic regression identified key independent risk factors for LNM, including FIGO staging, squamous cell carcinoma antigen (SCC-Ag), white blood cell count (WBC), neutrophil count (NEUT), hemoglobin (HGB) level, and prealbumin (PAB) level. These factors are significant in predicting LNM and emphasize their importance in clinical decision-making. AdaBoost's ability to predict LNM preoperatively, without invasive procedures such as lymph node dissection, can reduce treatment risks and improve patient outcomes. While other models, such as XGBoost, showed a marginally higher AUC in training, AdaBoost's performance in validation was comparable (P=0.18). Inflammatory and nutritional markers, such as WBC, NEUT, HGB, and PAB, were significant predictors and provide valuable insights into tumor progression. Despite the study's retrospective nature, the integration of larger, multi-center datasets, and multi-modal imaging could further enhance the model's accuracy and generalizability. This high-performance AdaBoost model offers clinical potential for refining personalized treatment strategies for CC patients.