Rotor−stator wet mills are commonly used in the pharmaceutical industry to reduce particle size and normalize API physical properties as a means to facilitate downstream drug product operations and/or achieve targeted in vivo product performance. Wet milling is robust, relatively easy to use, broadly applicable, and offers both financial and API physical property advantages over dry milling. Historical rotor−stator wet mill technologies are generally capable of achieving particles sizes down to ∼25 μm. Newer high-shear wet mills allow for a reduction of particle size down to ∼10−15 μm. In addition to the improved particle size reduction, recent wet mill designs better maintain geometric consistency across the product line, thereby providing enhanced scalability. A traditional scale-up approach for wet milling involved maintaining the tip speed of the rotor (assuming constant shear gap and thus constant shear rate) and would generally allow comparable terminal particle size (that near steadystate particle size where particle size reduction drastically slows) across scales. In order to predict the milling time upon scale-up the number of passes, or batch turnover, through the mill was kept constant. However, this prediction of the required milling time was often less successful than the prediction of the terminal particle size. Studies presented here confirmed the importance of maintaining constant rotor tip speed across scales to achieve the predicted terminal particle size and identified the importance of additional parameters to address particle breakage kinetics to allow prediction of the required milling time to achieve the target particle size. Additional aspects of hydrodynamics, shear rates, and equipment properties were assessed as part of these scale-up model optimization efforts. Specific processing parameters evaluated included flow rate, API slurry solids concentration, and starting particle size distribution. Ultimately, the Slot Event Model was developed to incorporate the critical geometric parameters by considering the frequency and the probability of a slot event. In addition to applying the revised model across scales, further model verification was achieved by evaluating custom rotor−stator mill heads. Studies with these custom mill heads provided insight into the importance of mill efficiency and slot events. This, in turn, allowed for more accurate scale-up of not only the terminal particle size but also the milling time required to achieve the target particle size. The success of the optimized model reduces the reliance on in-process controls or at-line testing for determining the end point of milling.