Background. Acute purulent infections are characterized by high microbial burden, immune dysregulation, and delayed tissue repair, leading to unpredictable treatment outcomes. Bacterial cellulose-based biomaterials, particularly BC-MXene biocomposites, demonstrate promising antibacterial and immunomodulatory properties; however, their effectiveness depends on complex interactions between microbiological, immunological, and reparative factors. Artificial intelligence (AI) algorithms offer new opportunities to integrate multidimensional experimental data and predict therapeutic effectiveness. Objective. To evaluate the use of artificial intelligence algorithms for predicting the antibacterial and immunological effectiveness of BC-MXene biocomposites in experimental models of acute purulent infection. Materials and Methods. An experimental study was performed on outbred white rats with mixed-etiology purulent infection induced by Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. Animals were divided into three groups receiving BC-MXene biocomposite dressings, bacterial cellulose-only dressings, or conventional local therapy. Microbiological dynamics, immune response indicators, cytokine trends, and morphological healing parameters were assessed over 14 days. Experimental data were digitized and analyzed using supervised AI algorithms, including logistic regression, random forest, and gradient boosting models, to predict treatment effectiveness. Results. BC-MXene treatment resulted in faster bacterial load reduction, improved immune response normalization, and accelerated tissue repair compared with control groups. By day 14, bacterial load decreased to 3.2×10² CFU/ml in the BC-MXene group compared with 1.0×10³ CFU/ml in the bacterial cellulose group and 4.5×10³ CFU/ml in the control group. The best-performing machine learning model (gradient boosting) achieved 93.5% accuracy and an AUC of 0.96. Conclusion. AI-based analysis provides an effective framework for predicting the antibacterial and immunological efficacy of BC-MXene biocomposites. The integration of advanced biomaterials with artificial intelligence enhances the objectivity and precision of experimental evaluation and supports future translational applications in purulent infection management.