Computing matrices is a crucial and prevalent topic in the field of data processing and artificial intelligence. One of the significant and effective methods for matrix manipulation is Singular Value Decomposition (SVD). Based on matrix computations using SVD, we can perform various complex operations such as dimensionality reduction, hidden information detection, optimization, and many other applications. Singular Value Decomposition is a valuable method in data science, allowing us to decompose a matrix into its fundamental components. Similar to Principal Component Analysis (PCA), SVD helps reduce the dimensionality of data while preserving the most important information. However, SVD can be applied to non-square, non-invertible matrices, and its ability to separate fundamental components enables us to analyze more complex data. SVD has a wide range of applications in practical scenarios.