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Application of machine learning (ML)-based algorithms in food drying is an exciting and innovative approach to advance the drying technology. In order to appropriately develop this novel approach in all aspects of food drying field, significant scientific research is required. The main aspects of food drying research are the determination of material properties, microstructural characterization, mathematical modeling, and process optimization. It is essential to express this fundamental information through ML-based algorithms to advance the food drying research. This paper aims to present a comprehensive review of the application of machine learning-based approaches in food drying modeling, property prediction, microstructural characterization, and process parameters optimization. Moreover, this paper discusses the possibilities and challenges to apply ML-based algorithms in multiscale modeling and microwave-based hybrid drying. It is expected that this review paper will be beneficial in advancing the machine learning-based food drying technology.

Application of machine learning-based approach in food drying: opportunities and challenges