Fouling is the unwanted accumulation of material on a processing surface which is an especially problematic issue in the food industry. Characterizing or predicting fouling through traditional methods or models is a challenge due to the complexity of fouling mechanisms. Machine Learning techniques can overcome this challenge by creating models for prediction directly from experimental data. Unfortunately, the results can be hard to interpret depending on the algorithm. Here, a soft sensor is generated from an extensive data set to predict the fouling of a model particle material system. This is performed inside two different pipe fittings, an inaccessible and accessible fitting (e.g., for sensor measurements). Additionally, Dimensional Analysis is conducted to identify the correlations responsible for fouling while keeping descriptors with physical meaning. The resulting dimensionless numbers are further processed by three machine learning algorithms: Linear Regression, Symbolic Regression, and Random Forest. The soft sensor generated using a Random Forest outperformed the other two regressors for the dimensional (Q2 = 0.90 ± 0.08) and for the dimensionless data (Q2 = 0.88 ± 0.09). The parameter time and particle mass fraction were determined to be most influential. Furthermore, seven dimensionless numbers were obtained allowing a reduced experimental design.