Since tofu quality and waste rates depend on coagulation conditions, technologies for predicting viscosity during the coagulation process are needed. This study focused on changes in light scattering during the coagulation process of tofu, which is a colloidal food. Laser scattering images were sequentially collected under various coagulation conditions to analyze these changes. To estimate the viscosity from the obtained images, regression models using pre-trained convolutional neural networks were constructed. Additionally, Long Short-Term Memory (LSTM) models were implemented to learn the temporal dependencies in the coagulation process, as tofu coagulation is a time-dependent phenomenon. Our model achieved an RMSE of 3.38 mPa·s, demonstrating the effectiveness of viscosity estimation using laser scattering images over the first 5 minutes after coagulation began. These results suggest potential contributions to quality improvement and waste reduction through real-time feedback of tofu coagulation status.