Accurate cardiovascular (CV) risk assessment is relevant for asymptomatic individuals, in particular for those at risk for cardiovascular diseases (CVD). Metabolic-associated fatty liver disease (MASLD), previuosly known as non-alcoholic fatty liver disease (NAFLD), is recognized as a critical independent risk factor for increased CV morbidity and mortality. With a prevalence of 25% in the general population, MASLD is the leading cause of chronic liver diseases and is strongly associated with the development of coronary artery disease (CAD). Coronary CT is widely used to detect CAD, and it can also assess liver steatosis, providing valuable prognostic information for at-risk patients. In this paper, we present a study on methods for performing prognostic stratification of CAD risk in asymptomatic MASLD patients using Machine Learning (ML) approaches. In particular, we conducted a retrospective analysis of clinical data from 60 patients who underwent Coronary CT at L'Aquila Hospital (Italy) between 2017 and 2021. Dataset includes significant features, such as radiodensity (Hounsfield Unit), calcium score (Agatston score), and liver fibrosis (Fib-4 score and APRI). We compared several ML algorithms (Logistic Regression, Support Vector Classifier (SVC), Random Forest, Extreme Gradient Boosting, K-Nearest Neighbors (KNN), Naive Bayes), with the main goal of performing binary classification tasks and creating a model able to differentiate between healthy patients and those affected by both MASLD and CVD. SVCs emerged as the best-performing models, achieving an AUC of 94%, an accuracy of 95%, and a recall of 94%. Our approach offers a robust and accurate tool for predicting CAD risk in MASLD patients, providing a valuable contribution to clinical practice for early CV risk stratification and management.