The design of corrosion-resistant alloys for demanding applications requires predictive tools that can link molecular-scale phenomena to component-level performance across vastly different length and time scales . This comprehensive review systematically examines the multiscale computational framework for corrosion-resistant alloy design, integrating density functional theory (DFT), ReaxFF molecular dynamics (MD), and phase-field modeling. DFT provides quantum-level insights into surface/adsorbate interactions, crystal structure information including lattice distortion and density of states, and formation energies essential for understanding corrosion initiation . ReaxFF MD enables dynamic simulation of chemical reactions, oxide growth, and dissolution kinetics at extended time scales, with recent studies on Ni-Cr alloys identifying three distinct voltage-dependent kinetic regimes governed by competing oxide growth, dissolution, and reprecipitation . Machine-learned interatomic potentials trained on DFT data have emerged as a bridge between quantum accuracy and atomistic-scale simulation, enabling molecular dynamics simulations of complex oxide microstructures . Phase-field modeling has matured as a powerful mesoscale technique for simulating autonomous evolution of corrosion pits and localized corrosion morphologies, with grand-potential formulations enabling efficient simulation in multiphase alloys . The integration of these approaches with thermodynamic databases (CALPHAD) and finite element methods enables prediction of microstructural evolution, stress corrosion cracking, and component lifetime . This review concludes that next-generation alloy design requires seamless coupling of quantum, atomistic, mesoscale, and continuum methods, supported by machine learning and experimental validation.