This Review surveys a broad range of machine-learning applications in molecular excited-state research, including predicting molecular properties and searching for new optoelectronic materials. The authors critically discuss machine-learning developments to track their progress, assess the current state-of-the-art, and highlight the critical issues to solve in the future.