Matrix Variate Distribution is a generalization of Multivariate distributions to Matrix-valued random variables, capturing dependencies across both rows and columns. It is particularly useful in Multivariate Statistics and Machine Learning for Modeling Data with Inherent matrix structures, such as images or Multivariate Time Series.