Food drying has an impact on the final product's quality, energy usage, and environmental sustainability, making it a crucial step in food preservation. Although crucial, traditional optimization methods usually fall short in the complex, nonlinear, and multi-objective process of food drying. Algorithms inspired by nature that simulate biological, chemical, and physical systems have become popular tools for enhancing these procedures; they perform better when faced with multiple variables and conflicting objectives. The application of nature-inspired optimization techniques, including artificial neural networks, genetic algorithms, particle swarm optimization, and the non-dominated sorting genetic algorithm II, to food drying is critically examined in this work.

The main focus of the study is on the theoretical foundations of these algorithms, their specific application in food drying, and the benefits and drawbacks of each method. Furthermore, we examine recent case studies that demonstrate the practical use of these strategies in food processing plants to enhance environmental impact, energy efficiency, and product quality. The findings imply that if these advanced optimization algorithms are included into computer-integrated production systems, the food drying industry may embrace more cost-effective and ecologically friendly procedures. The scalability of these technologies for industrial applications is also critically assessed, exposing practical challenges and providing suggestions for future research directions. This in-depth examination seeks to serve as a helpful tool for experts and academics curious in the food dying optimization, presenting insights that are board and deep, suitable for a multidisciplinary audience.