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Cognitive impairments are generally observed in patients with schizophrenia. However, it is unclear whether neurocognitive dysfunction can predict the efficacy of antipsychotics for first-episode schizophrenia (FES). Machine learning methods provide a relatively unbiased approach when evaluating heterogeneous data, especially when building multifactor prediction models. This study conducted a secondary analysis based on the Chinese FES Trial (CNFEST), which was a 1-year study involving a randomized controlled trial for the first eight weeks followed by a 48-week open-label observation. The current study aimed to build a prediction model of eight-week antipsychotic response based on baseline clinical and demographic features. Six machine learning algorithms, including random forest, eXtreme gradient boosting (XGBoost), logistic regression, linear support vector machine (SVM), radial basis function SVM and poly SVM were applied and compared to draw the prediction model. The predictive effects were evaluated by balanced accuracy, sensitivity and specificity. The predictive factors were compared with F scores. A total of 450 qualified subjects contributed to the model. The prediction model constructed via XGBoost algorithm had the highest accuracy (68.8%) and prognostic certainty (44.3%) among all the algorithms. The baseline neurocognitive tests with strong predictive significance were the Grooved Pegboard Test, Trail Making Test Part A, Paced Auditory Serial Addition Test, Brief Visuospatial Learning Test, Hopkins Verbal Learning Test and Color Trails Test. This study emphasizes the importance of fine motor skills, verbal learning, visual learning, working memory and attention for the response of drug-naïve FES patients to antipsychotics. The model generated by XGBoost, which shows preferable accuracy, provides psychiatric practitioners with a possible way to predict efficacy for FES patients.