Corrosion remains a persistent and costly challenge in infrastructure and manufacturing systems, affecting the longevity, safety, and efficiency of metallic components and structures. Traditional corrosion modeling techniques, while useful, often fall short in delivering accurate, real-time predictions due to the complex and multifactorial nature of corrosion processes. In recent years, artificial intelligence (AI) has emerged as a transformative tool in augmenting corrosion prediction models by enabling data-driven analysis, pattern recognition, and continuous learning from diverse datasets. This systematic review explores the integration of AI techniques—such as artificial neural networks (ANNs), support vector machines (SVMs), fuzzy logic, decision trees, and deep learning algorithms—into corrosion modeling across key industrial domains. Using the PRISMA methodology, peer-reviewed articles published between 2014 and 2023 were sourced from databases including Scopus, Web of Science, and IEEE Xplore. A total of 85 high-impact publications were analyzed, focusing on AI-based models for corrosion prediction in pipelines, reinforced concrete structures, offshore platforms, and manufacturing equipment. The review identifies that AI-augmented models significantly outperform traditional empirical and physics-based models in terms of predictive accuracy, adaptability to changing environmental conditions, and scalability across different use cases. Among the findings, artificial neural networks demonstrate high proficiency in capturing non-linear relationships in corrosion kinetics, while ensemble methods and hybrid models combining AI with finite element or electrochemical simulations offer superior predictive robustness. However, challenges such as data scarcity, model interpretability, and generalization across different materials and environments persist. The review emphasizes the need for standardized datasets, model validation frameworks, and interdisciplinary collaborations between materials scientists and AI specialists. This study highlights the growing potential of AI to revolutionize corrosion management through predictive maintenance, lifecycle optimization, and intelligent infrastructure design. It calls for increased investment in AI-driven research and development to address corrosion-related economic losses and safety risks. The review ultimately provides a foundation for future work toward the deployment of smart corrosion monitoring systems in Industry 4.0 ecosystems.