A. J. Fernando
Microwave drying (MWD) is a promising technique for dehydrating agricultural products due to its rapid volumetric heating, high energy efficiency, and superior preservation of product quality. However, the complex and nonlinear nature of microwave–material interactions, along with the spatial and temporal variability of dielectric properties, presents significant challenges for process modeling, control, and optimization. Traditional mathematical models often fall short in capturing these dynamics, which limits their use in adaptive or real-time process regulation. The goal of this review is to provide a comprehensive synthesis of artificial intelligence (AI) techniques applied to the microwave drying of agricultural products, focusing on predictive modeling, intelligent control, and optimization through a bibliometric analysis that covers literature from 2014 to 2024. Techniques such as artificial neural networks (ANNs), support vector machines (SVMs), adaptive neuro-fuzzy inference systems (ANFIS), and evolutionary algorithms are assessed for their effectiveness in modeling drying kinetics, predicting quality attributes, and supporting closed-loop control. Recent advancements in hybrid and ensemble models, real-time sensor integration, and multi-objective optimization are also examined. The review highlights current limitations in AI-based drying systems, including data scarcity, overfitting, poor model interpretability, and limited real-time deployment. It proposes strategic future directions, such as the adoption of explainable AI, digital twin frameworks, embedded edge computing, and sensor fusion for autonomous control. This work highlights the transformative potential of AI in developing intelligent, scalable, and energy-efficient MWD systems that align with the goals of Industry 4.0 and sustainable food engineering. • AI methods predict microwave drying behavior more effectively than classical models. • ANNs are widely used to model drying rates and moisture ratios from microwave parameters. • Hybrid AI models improve optimization of microwave power and temperature settings. • AI-driven control systems use sensor feedback and machine learning to adjust drying. • AI enables multi-objective optimization of energy use, time, and product quality.