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The abundance of medical data available, ranging from electronic health records to medical images, presents a unique opportunity to gain valuable insights into disease processes, improve treatments, and enhance patient outcomes. However, the complexity, high dimensionality, and heterogeneity of these datasets pose significant challenges to their analysis and interpretation. One technique that has gained popularity for addressing these challenges is data embeddings. Data embeddings are low-dimensional representations of high-dimensional data that preserve the underlying structure and relationships between data points. In the medical domain, data embeddings have found numerous applications, such as disease diagnosis, patient risk stratification, and drug discovery. This survey paper aims to provide a comprehensive overview of data embeddings techniques and their applications in the medical domain. The paper introduces the concept of data embeddings and their properties, and provides a detailed discussion of popular embedding techniques, including principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders. The paper also reviews various applications of data embeddings in the medical domain, such as disease diagnosis, patient clustering, and drug discovery. The paper concludes with a discussion of future directions and emerging trends in data embeddings for medical applications, emphasizing the need for more robust and interpretable embedding techniques and the importance of considering clinical context when developing and applying these techniques.