The kinetics of milk coagulation are complex and still not well understood. A deeper understanding of coagulation and the impact of the relevant factors would aid in both cheese manufacturing and also in determining the nutritional benefits of dairy products. A method using confocal microscopy was developed to follow the movement of milk fat globules and the formation of a milk protein network during the enzyme-induced coagulation of milk. Image processing methods were then used to quantify the rate of coagulation. It was found that the texture of the protein network is an indicator of the current status of the milk gelation, and hence can be used to monitor the coagulation process. The imaging experiment was performed on milk gels with different concentrations of the coagulation enzyme, chymosin. Rheological measurements were taken using free oscillation rheometry to validate the imaging results. Both methods showed an inverse relationship between rennet concentration and the coagulation time. The results from the imaging study were used to create a computational model, which created simulated images of coagulating milk. The simulated images were then analyzed using the same image analysis algorithm. The temporal protein network texture behavior in the simulated images followed the same pattern as the protein texture in the confocal imaging data. The model was developed with temperature and rennet concentration as user inputs so that it could be implemented as a predictive tool for milk coagulation.