Aim. To develop, optimize, and evaluate effective in silico models for predicting the neuroprotective and anxiolytic properties of natural compounds using Scutellaria baicalensis as a case study. Methods. Construction and validation of machine learning models. Results. Three machine learning models, constructed using the Random Forest, XGBoost, and LightGBM algorithms, were developed for in silico prediction of the neuroprotective and anxiolytic activity of natural compounds. The classifiers achieved an accuracy of 75–78%. A binary classification approach was proposed, incorporating molecular descriptors and structural fingerprints, which, after preprocessing and optimization, enabled the identification of compounds with potential neuroprotective activity. The study confirms the effectiveness of these modeling approaches in predicting the neuroprotective, anxiolytic properties of S. baicalensis compounds. Application of the models to known phytochemicals from this plant verified previously reported bioactive substances: of 78 analyzed compounds, 46 were predicted to be potentially active. Conclusions. The in silico prediction of neuroprotective properties of bioactive compounds shows promise for screening and identifying phytocomplexes, particularly for applications in modern medicine such as the prevention and management of PTSD and other neurological disorders.