Non-Alcoholic Fatty Liver Disease (NAFLD) has emerged as one of the most prevalent liver disorders globally, affecting nearly one-third of the population, with particularly high incidence rates in countries like the UK. Despite its widespread occurrence, accurate estimation of its prevalence remains a challenge. Earlystage NAFLD, typically characterized by simple steatosis, can silently progress to more severe conditions such as non-alcoholic steatohepatitis (NASH), fibrosis, and cirrhosis if left untreated. This progression significantly compromises liver function and increases the risk of cardiovascular complications. However, current diagnostic methods, including magnetic resonance spectroscopy and ultrasound imaging, are often limited by cost, accessibility, and diagnostic specificity. Given the clinical urgency and the limitations of conventional diagnostics, this study addresses the critical need for an accessible and accurate method to detect early-stage liver disease—specifically, to predict NASH within the NAFLD spectrum. We propose a machine learning-based approach that leverages clinical and pathological data, including blood parameters and ultrasound-derived tissue characteristics, to support early detection. Using a dataset of 181 patients, we applied preprocessing techniques such as normalization and categorical encoding to prepare the data for modelling. Features such as integrated backscatter (IB), Q-factor, and homogeneity factor (HF) were extracted to quantify liver tissue characteristics. Support Vector Machine (SVM), chosen for its balance of simplicity and efficiency in handling high-dimensional datasets, was employed for classification and regression tasks. Experimental validation using Python-based implementations demonstrated the model’s effectiveness, achieving an average accuracy of 89.95% across both clinical and imaging-derived datasets. This study underscores the potential of machine learning in improving early diagnosis of liver diseases and reducing their long-term clinical burden.