The various physicochemical and textural qualities of pantoa (Indian dairy dessert) at different stages of deep‐fat frying (DFF) were studied. Fat uptake was inversely related to moisture content. Pore formation during DFF resulted in an expansion ratio of 1.25–1.27, and a consequent decrease in apparent density from 1156 to 682 kg/m3. As crust formed, hardness of the product increased from 1.28 N to 6.29, 6.89 and 8.23 N at 125, 135 and 145C, respectively, after 480 s of frying. The influences of frying time and temperature on the physicochemical and textural attributes were predicted. Changes in moisture content and hardness, respectively, followed first and fractional conversion first order kinetics, while all other attributes were best described by a logistic model. The artificial neural network (ANN) models, with one hidden layer and 5–9 neurons were found to be superior to the mathematical models in predicting quality attributes as a function of process conditions. Also, using the physicochemical attributes of pantoa, its texture (hardness) was predicted using multiple linear regression and ANN models. Practical Applications Manufacture of pantoa is currently dominated by small‐scale unorganized enterprises leading to non‐optimized process conditions and widely varying product quality. The present study aims at describing the quality changes in the product during the deep‐fat frying using mathematical models in a fryer. The effects of process parameters such as temperature and time on the various physicochemical and textural qualities were evaluated. The prediction of texture as a function of frying conditions, as well as, physicochemical attributes will be a useful and non‐destructive method of evaluating quality. The mathematical models provide information to food processing professionals to help in optimizing the frying conditions of pantoa in order to maximize energy efficiency and consumer acceptance.