Computational modeling and simulation have emerged as indispensable tools for understanding corrosion phenomena across multiple length and time scales, bridging the gap between atomistic mechanisms and component-level lifetime prediction . This comprehensive review systematically examines the multiscale computational framework for corrosion research, from electronic structure calculations to continuum-scale predictive models. Density functional theory (DFT) provides quantum-level insights into corrosion inhibitor adsorption, with frontier molecular orbital analysis revealing that a lower energy gap (ΔE = ELUMO - EHOMO) correlates with enhanced inhibition efficiency through increased molecular reactivity . Reactive molecular dynamics (ReaxFF) enables tracking of chemical reactions, bond formation and breaking during initial corrosion stages, though time scales remain considerably shorter than realistic corrosion phenomena . Multiscale approaches integrating atomistic insights with finite element method have emerged, preserving grain boundary crystallography while enabling realistic large-scale simulations of corrosion-induced intergranular strain . Phase-field modeling has become a powerful mesoscale tool for simulating autonomous evolution of corrosion pits, capturing the complex interactions between electrochemical processes and mechanical deformation . Machine learning applications have revolutionized lifetime prediction, with hybrid models achieving R² > 0.99 for corrosion rate forecasting and service life prediction . The review concludes that next-generation corrosion modeling requires integrated frameworks combining physics-based mechanistic understanding with data-driven approaches.