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Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide. We explore the application of statistical shape modeling (SSM) as a powerful tool in cardiac anatomy assessment, facilitating innovative approaches to diagnosis and treatment. SSM uses advanced mathematical and statistical techniques to understand the geometric properties of anatomical structures across populations. By identifying significant shape parameters, it captures and quantifies subtle variations that may elude traditional approaches. We discuss its evolution, from landmark-based methods to point distribution models for establishing the point-to-point correspondence crucial for accurate shape analysis. We delve into the statistical techniques used to measure shape variability, with a focus on principal component analysis for dimensionality reduction. Key evaluation metrics in the assessment of model performance, such as compactness, generalization and specificity, are reviewed. The clinical utility of SSM across the spectrum of CVDs is examined, covering diagnosis, risk stratification, treatment optimization, follow-up and research applications. Future directions, including the development of multi-label models, integration of deep learning approaches, and spatio-temporal SSM to capture dynamic changes in cardiac geometry, are considered. Through this narrative review, we aim to underscore SSM's promise as a powerful tool in combating CVDs and advancing personalized medicine, ultimately improving patient outcomes.