Cardiovascular disease is the leading cause of death worldwide. There is a need for advanced, precise, and scalable diagnostic procedures to identify and address it promptly. This study uses clinical and demographic data from the Cleveland Heart Disease dataset to develop a new deep learning method for predicting heart disease. The approach facilitates transfer learning and tabular transformer architectures, such as SAINT and FT-Transformer. Our approach distinguishes itself from conventional machine learning methodologies by utilizing pretrained representations and attention mechanisms to identify intricate relationships between samples and attributes. Traditional machine learning algorithms depend on a limited set of fabricated features and interactions. Our systematic methodology incorporates principal component analysis (PCA) for dimensionality reduction, domain-specific feature engineering, modifications to the transformer layer for binary classification, and improved data preparation. Using a fixed base encoder significantly reduces training time and improves generalization when learning a specific classification head. Extensive testing revealed that the SAINT-based model outperformed random forests, logistic regression, and CNN-MLP models. The study’s findings suggest that tabular converters can accurately interpret structured clinical data and that transfer learning is a beneficial approach for medical predictive analytics. The proposed method is a versatile and scalable solution for various diagnostic applications in healthcare AI, facilitating the prediction of heart disease.