Biomarkers have been extensively explored as robust classification features in training neural network-based and other machine learning and artificial intelligence-driven prognostic models, particularly within the domain of personalized nutrition. In this chapter, we develop and analyze cascaded support vector machine (SVM)-based classifiers for the automated diagnosis of metabolic syndrome. Specifically, utilizing blood test data, we achieve an average classification accuracy of approximately 84% for body mass index (BMI). Likewise, cascaded SVM-based classifiers demonstrate a 74% accuracy in classifying systolic blood pressure. Furthermore, we design and implement a predictive system that attains an accuracy of 84% in forecasting metabolic syndrome. This system leverages not only BMI classification but also predictive insights from blood test parameters, including total cholesterol, triglycerides, and glucose levels. To ensure the self-containment of this chapter, fundamental concepts pertaining to metabolic syndrome are summarized, and a review of prior relevant studies is provided. Lastly, conclusions are drawn, and potential directions for future research in this area are discussed.