Bayesian Hierarchical Modeling is a statistical approach that allows for the modeling of complex data structures by incorporating multiple levels of variation and uncertainty. It provides a framework for borrowing strength across groups or levels, leading to more robust parameter estimates and predictions, especially in situations with limited data at individual levels.