Objective To meet the clinical need for dynamic monitoring on lactate metabolism in septic shock patients, a time-series prediction model based on a long short-term memory (LSTM) network was developed to predict 24-hour lactate clearance rate at admission. Methods A multi-stage retrospective cohort design was adopted to enroll septic shock patients admitted to the department of critical care medicine of a hospital from January 2018 to September 2024. By conducting univariate analysis and LASSO combined feature screening, predictive factors were extracted from multidimensional clinical data. An end-to-end LSTM framework (two-layer 64/32 units, dropout rate=0.3) was constructed. A sliding window strategy (six-hour step size) was adopted for dynamic prediction and compared with traditional logistic model in terms of three dimensions: calibration (Brier score), discrimination (area under the curve ［AUC］ of time-dependent receiver operating characteristic ［ROC］), and clinical practicality (decision curve analysis). Consistency between model prediction result and actual lactate clearance rate was analyzed, and the accuracy of prediction was evaluated. Results A total of 112 septic shock patients were enrolled in the analysis, including 65 males and 47 females, with an average age of (67.35±7.28) years. 65 patients were assigned in the lactate good clearance rate group (lactate good clearance rate ≥10%) and 47 in the lactate poor clearance rate group (lactate good clearance rate ＜10%); 78 patients were in the training set and 34 in the validation set. Time-depen-dent AUC analysis revealed that the predictive performance of the LSTM model in the time windows of 6, 12, and 24 hours were 0.89 (0.85-0.93), 0.91 (0.88-0.95), and 0.92 (0.89-0.96), respectively, superior to the logistic regression model (ΔAUC=0.085, P<0.01). The core predictive factors included APACHE Ⅱ score (OR=1.38), lactate level at admission (OR=1.65), vasoactive drug dosage (OR=1.42), and 6-hour fluid resuscitation dosage (OR=1.35). The Pearson correlation coefficient between the predicted value of the model and the actual 24-hour lactate clearance rate was 0.83 (P<0.001), with an average absolute error of 8.2%. Decision curve analysis confirmed that when the threshold probability was 15%-60%, the LSTM model could increase clinical net benefits by 27.3%. The validation of each subgroup showed that the model maintained the optimal predictive performance (AUC=0.87) in the lung infection subgroup (n=16). Conclusion The LSTM-based dynamic prediction model for predicting 24-hour lactate clearance rate through integrating early admission indicators demonstrates excellent predictive performance and clinical application value, which can provide important reference for individualized treatment decisions in septic shock patients.