Transductive learning focuses on leveraging specific test data during the training phase to improve prediction accuracy, unlike inductive learning which generalizes from training data to unseen data. It is particularly useful in scenarios with limited labeled data, where the goal is to make predictions on a specific set of test instances rather than on any possible unseen data.