Modern medical imaging technologies offer a wide array of methods for assessing various physical parameters. Although liver biopsy is widely regarded as the gold standard for staging fibrosis, it lacks the ability to evaluate the mechanical properties of tissues—particularly stiffness—which is vital in the diagnosis and management of conditions such as fibrosis, tumors, and injuries. Recent innovations in ultrasound-based imaging, such as elastography, have introduced non-invasive solutions for measuring tissue elasticity. This advancement represents a major leap forward in the field of diagnostic imaging. The present study aims to predict liver stiffness using transient elastography (FibroScan) data combined with machine learning algorithms. Five supervised classification models were employed—Random Forest, Logistic Regression, Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—to analyze the imaging data. Dimensionality reduction techniques and feature selection were used to identify the most informative attributes. Among the models, Logistic Regression demonstrated superior performance, achieving 90% accuracy in predicting liver stiffness. This system highlights the effectiveness of integrating machine learning with non-invasive imaging techniques for early and reliable detection of liver stiffness, thereby supporting timely medical intervention.