Manifold regularization is a semi-supervised learning technique that incorporates the geometry of the data distribution into the learning process by leveraging both labeled and unlabeled data. It extends traditional regularization methods by adding a manifold-based penalty term, encouraging the learned function to be smooth with respect to the underlying data manifold structure.