Non-linear manifold learning is a dimensionality reduction technique that seeks to uncover the low-dimensional structure embedded within high-dimensional data by assuming that the data lies on a curved manifold. This approach is particularly useful for capturing complex, non-linear relationships in data that linear methods like PCA cannot handle effectively.