Torularhodin is a bioactive carotenoid synthesized by certain microorganisms through complex cellular processes regulated by factors like nutrient availability. However, enhancing torularhodin production is a challenging task that requires costly and time-intensive experimental approaches. To address these limitations, computational modeling and simulation have become valuable tools for predicting and optimizing carotenoid biosynthesis. Among these techniques, polynomial models derived from multiple regressions provide useful insights but often struggle with the nonlinear nature of biological systems. In contrast, Artificial Neural Networks (ANNs) offer a more flexible alternative, improving predictive accuracy where traditional models fall short. This study aimed to optimize torularhodin production in