Mills and classifiers are primarily used for particle size reduction and separation across various industries, including paper, paint, plastic, pharmaceuticals, ceramics, cosmetics, foods, and fine chemicals. They are typically used to achieve a targeted particle size distribution or specific particle shape. Numerical modeling has the potential to inform equipment design to control particle transport and predicting high-wear spots. Given the substantial volume of particles processed in industrial contexts, even marginal improvements in grinding efficiency can lead to significant economic benefits. This review summarizes advancements in understanding and modeling key processes taking place at the scale of individual particles and coarse grain approaches to simulate processes at the scale of industrial units. The review concludes with a brief perspective on future research directions, including the use of machine learning for constitutive modeling and design optimization.