Cardiovascular disease (CVD) remains a leading cause of mortality, yet accurate and interpretable risk prediction poses significant clinical challenges. While machine learning (ML) models show promise in identifying complex patterns, their adoption in healthcare is often limited by a lack of transparency. This paper proposes a novel explainability framework integrating Generative Topographic Mapping (GTM) and Kernel Generative Topographic Mapping (kGTM) to visualize and analyze latent cardiac risk patterns. Unlike traditional dimensionality reduction methods, our approach unifies probabilistic modeling, visual explainability, and clinically grounded subgroup analysis within a single interpretable latent representation. We employ a structured dataset of 1,000 patients with 12 clinical features to demonstrate how GTM and kGTM project high-dimensional patient data into a structured 2D space. This methodology enables direct visualization of patient clusters, classification boundaries, and the influence of individual features, thereby uncovering interpretable regions and clinically meaningful feature interactions directly from the topographic map. This approach provides robust and trustworthy perspectives on ML model behavior for cardiovascular risk assessment. Our dual use of GTM and kGTM allows for adaptive modeling, facilitating the identification of outliers and borderline cases. This dual nature enhances understanding of both the feature space distribution and the uncertainty of the model’s decisions, enabling partial explainability by highlighting relevant features and structural patterns in the data.