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This study evaluated the applicability of machine learning models for supporting the diagnosis of Feline panleukopenia virus (FPV) infection in cats based on routine haematological and blood biochemical data. A retrospective study was conducted on 60 cats, including 30 healthy cats and 30 FPV-infected cats, collected from veterinary clinics in Ho Chi Minh City. The data were preprocessed, standardized, and analyzed using seven machine learning algorithms, including Logistic Regression, KNN, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, and Naïve Bayes, combined with k-fold cross-validation. The results showed that Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and SVM achieved an accuracy of 92%, with Random Forest demonstrating optimal performance (AUC = 1.00). Feature importance analysis and PCA identified Mon#, WBC, and GLU as the main predictive biomarkers, while also demonstrating a clear separation between healthy and FPV-infected cats, confirming the potential application of machine learning in veterinary clinical practice.