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The aim of this article is to study the microwave-assisted foam mat drying of papaya to form papaya powder. The process of foam mat drying of papaya using microwaves was modeled by machine learning approaches like artificial neural network (ANN), support vector regression (SVR), and Gaussian process regression (GPR). Effect of microwave power (480-640 W), inlet air temperature (40-50 C), and thickness of foam (2-4 mm) on the rate of drying were studied. The performance of the models was evaluated on the basis of different performance matrices including root mean square error (RMSE), coefficient of determination (R 2 ), model predictive error, and Chi-square (χ 2 ). The microwave heating of the papaya foam reduced the drying time manifold. All three machine learning approaches were able to predict the drying process efficiently.SVR showed the best performance (R 2 = 0.96; RMSE = 0.03) followed by GPR (R 2 = 0.92; RMSE = 0.04) and ANN (R 2 = 0.91; RMSE = 0.04). SVR-based model was simulated to predict the effect of power, temperature, and thickness on drying rate.Machine learning approaches can be efficiently used for modeling and microwaveassisted foam mat drying. SVR-based model proves to be a good alternative of ANN.Practical Applications: Foam mat drying is the method of dehydrating for heatsensitive and viscous materials which cannot be dried by other conventional methods. It is cost-effective, simple, and provides high product quality. The use of microwave-assisted drying decreases the drying time manifold. Modeling of the drying process is significant for its scale-up to industrial scale. Machine learning techniques have the capability of learning the hidden factors involved in the process and thus provide better predictions as compared with statistical regression methods. In this study, three machine learning methods (artificial neural network [ANN], support vector regression [SVR], and Gaussian process regression [GPR]) were compared for their efficiency in modeling the foam-mat drying of papaya pulp using microwaves.SVR showed the best performance as compared with ANN and GPR.