The liver is the body's most essential organ, and all human activities are interrelated with normal liver function. Any malfunction of the liver may lead to fatal diseases; therefore, early detection of liver abnormalities is essential. Modern medical imaging techniques combined with engineering procedures are reducing human suffering caused by liver disease. This study uses multiple classifiers to detect liver cirrhosis in ultrasonic images. The ultrasound images were obtained from The Cancer Imaging Archive database. A gray-level co-occurrence matrix (GLCM) and statistical approaches are used to extract features from normal and liver-cirrhosis images. The extracted GLCM features are normalized and classified using nonlinear regression, linear regression, logistic regression, Bayesian Linear Discriminant Classifiers (BLDC), Gaussian Mixture Model (GMM), Firefly, Cuckoo search, Particle Swarm Optimization (PSO), Elephant search, Dragon Fly, Firefly GMM, Cuckoo search GMM, PSO GMM, Elephant search GMM, and Dragon Fly GMM classifiers. Benchmark metrics, such as sensitivity, specificity, accuracy, precision, negative predictive value, false-negative rate, balanced accuracy, F1 score, Mathew correlation coefficient, F measure, error rate, Jaccard metric, and classifier success index, are assessed to identify the best-performing classifier. The GMM classifier outperformed other classifiers for statistical features, and it achieved the highest accuracy (98.39%) and lowest error rate (1.61%). Moreover, the Dragon Fly GMM classifier achieved 90.69% for the GLCM feature used to classify liver cirrhosis.