Heart disease remains a leading cause of morbidity and mortality globally, necessitating accurate and early diagnostic tools to improve patient outcomes. The escalating volume of healthcare data, coupled with advancements in computational capabilities, has positioned machine learning (ML) as a transformative approach for enhancing the classification of cardiac conditions. This article provides a comprehensive evaluation of machine learning models, particularly focusing on Multilayer Perceptron (MLP) and Support Vector Machine (SVM) architectures, for their efficacy in classifying heart disease. We delve into the methodologies employed, including feature selection and model training, and analyze their performance metrics. The discussion highlights how these advanced computational techniques contribute to more precise, efficient, and reliable diagnostic support systems, thereby aiding clinicians in early detection and personalized treatment strategies.