Reactive oxygen species (ROS) play a central role in the pathogenesis of metabolic disorders such as type 2 diabetes, obesity, and nonalcoholic fatty liver disease. Excessive ROS generation leads to chronic inflammation and cellular damage, contributing to disease progression. Targeting ROS-mediated inflammation offers a promising therapeutic strategy. Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are revolutionizing biomedical research by enabling the discovery of novel therapeutic targets, predictive biomarkers, and drug candidates with high precision and speed. This chapter highlights how AI, ML, and DL are being harnessed to model ROS dynamics, predict inflammatory pathways, and screen compounds for antioxidant and anti-inflammatory potential. Supervised learning algorithms help in classifying disease phenotypes based on oxidative stress markers, while unsupervised and reinforcement learning approaches unravel complex molecular interactions. DL models, especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are used in image analysis and temporal gene expression profiling, facilitating early diagnosis and intervention. Integrating multi-omics data through AI-driven models enables a systems-level understanding of inflammation and metabolic dysregulation. Furthermore, AI-assisted drug repurposing and de novo drug design are accelerating the identification of molecules that modulate ROS and inflammatory mediators. Despite challenges like data standardization and model interpretability, AI holds transformative potential in mitigating ROS-mediated inflammation in metabolic disorders. This interdisciplinary approach could pave the way for precision medicine strategies and more effective therapies in the near future.