Objective Constructing a predictive model for early enteral nutrition aspiration risk in critically ill neurosurgical patients based on machine learning algorithms and verifying its predictive performance. Method A retrospective study was conducted on 322 critically ill neurosurgical patients admitted to Zhangjiagang Aoyang Hospital from January 2023 to December 2024. The patients were randomly divided into a modeling group n = 258 and a validation group n = 64 in a 4 ∶ 1 ratio using the retention method. Collect clinical data from patients screen key variables through LASSO regression analysis conduct multiple factor analysis based on relevant risk factors use machine learning algorithms to construct an early enteral nutrition aspiration risk prediction model and verify the performance of the model. Result This study included 322 patients of whom 87 experienced aspiration with an incidence rate of 27. 02%. LASSO regression analysis identified a total of 9 key variables. Multivariate analysis results showed that age consciousness status mechanical ventilation smoking history NRS 2002 score and number of comorbidities were independent risk factors for early enteral nutrition aspiration in critically ill neurosurgical patients P < 0. 05 . Based on this 6 machine learning models were constructed. Receiver working characteristic curve analysis results showed that the area under the curve of the modeling and validation groups of the 6 machine learning models was>0. 7 with the XGBoost model having the highest predictive performance The decision curve analysis results show that within the high -risk threshold range of 0-1. 0 all six models can achieve higher standardized net returns compared to those with and without intervention The ten fold cross validation results showed that there was no significant fluctuation in the area under the curve of the six machine learning models indicating good model fitting. Conclusion A prediction model for early enteral nutrition aspiration risk in critically ill neurosurgical patients based on machine learning algorithms is relatively accurate in predicting aspiration risk. Among them XGBoost has the best predictive performance but considering clinical practicality and convenience it is recommended to build a prediction model based on Logistic algorithm which is second only to XGBoost in performance in order to formulate corresponding preventive measures and reduce aspiration risk in clinical practice.