Materials discovery faces a huge and complex high-dimensional search space, from which the fast and effective selection of new materials with target properties is a major challenge in materials development. Machine learning can predict the performance of unexplored materials via establishing the relationship between features and target performance through algorithms based on the existing data. However, there are a relatively few known data for materials, and the machine learning models have a relatively low prediction accuracy, thus making it difficult to achieve an effective guidance for experiments or calculations. To address this problem, active learning was introduced for assistance, and the experimental design step was added to the traditional iterative feedback to select the experiments for target enhancement to supplement and to achieve the optimization of material performance. This review mainly represented recent progress on active learning-assisted materials development from three aspects, i.e., single-objective optimization, multi-objective optimization, and curve optimization.