This paper reviews recent advances in the application of machine learning (ML) methods to quantitative structure-property relationship (QSPR) modelling and molecular generation in surfactant science. As the variety of surfactant species and annual production volumes continue to grow, their proper characterization becomes increasingly important. Artificial intelligence (AI) technologies in chemistry represent a promising set of tools for addressing such tasks, in which ML plays a key role. The sc