Manifold Learning is a type of unsupervised learning that seeks to uncover the low-dimensional structure embedded within high-dimensional data by assuming that the data lies on a manifold. It is particularly useful for dimensionality reduction and visualization, helping to preserve the intrinsic geometry of the data while reducing computational complexity.