Due to the complex and uncertain nature of geomaterial properties, establishing representative parametric models between clay parameters is a challenging task. Nonparametric machine learning offers an accessible approach to develop empirical transformation models of clay parameters based on the available measurement. In this study, nonparametric modeling of clay parameter relationships via broad learning system (BLS) is introduced. Broad learning architecture provides an effective tool for nonparametric modeling based on noise-corrupted data. The architecture of deep learning is configured with stacks of hierarchical layers, which consume expensive computational cost for network training. In contrast, the network of BLS is established in a flat architecture and it can be modified incrementally. As a result, the broad learning flat network can be reconfigured efficiently to accommodate additional training data. To demonstrate the performance of the learning algorithm for clay parameters, the comprehensive global database CLAY/10/7490 with 7,490 data points from over 250 studies worldwide is utilized and analyzed.