Various methodologies have been proposed in the literatures to predict the particle size distribution (PSD) in agglomeration processes. However, there is no universal model that can completely satisfy industrial needs due to the complexity of the agglomeration process and the unclear mechanisms of particle growth. A systematic approach using an artificial neural network (ANN) model to predict PSD parameters of different agglomeration processes in high shear mixer was developed. In order to reduce input dimensions, several dimensional or dimensionless parameter groups are defined to represent the raw material properties, equipment geometries, and operation conditions as inputs. According to statistical analysis, an ANN model combined with the key parameter definitions can predict product mean particle size (MPS) within an average absolute relative error (AARE) of 7.18%, a standard deviation (sigma) of 7.72%, and a correlation coefficient (R) of 0.9657 (while for distribution span prediction AARE is 5.76%, sigma is 5.88%, and R is 0.9759). The results demonstrate that the ANN model developed in this work can predict the product PSD parameters with good accuracy over a wide range of operating conditions, material properties, and equipment scales.