Over the last decade, a considerable amount of research has documented the application of machine learning (ML) and its potential for cleaner production of sustainable construction materials particularly on geopolymers. Conceptually, the use of ML could help optimize the mixture composition, predict the property and performance of geopolymers materials. However, existing studies seem to mainly concentrate on geopolymer concrete and thus overlook other forms such as mortar and paste, and the data requirements of ML. In addressing the gaps, the aim of this study is to provide a current status of art on the use of ML on geopolymer materials by specifically exploring (i) the progression of ML in geopolymer materials from 2012-2023; (ii) the forms and types of geopolymer being researched using ML; (iii) the data sources and sizes, and ML algorithms being used; and (iv) the tasks being performed using ML. The overall findings show that ML are primarily utilized for predicting geopolymer properties, particularly compressive strength, while their potential in mixture optimization and structural maintenance remains largely untapped. Additionally, the small training datasets and the predominant reliance on data from previous publications in most studies underscore the limited utilization of field data. In conclusion, this study informs researchers of the current challenges in the application of ML for geopolymer materials and proposes directions for future research in using ML for improved property prediction and mixture optimization of sustainable geopolymer materials.