Numerous experimental methods, often used in combination, are employed in corrosion studies, including electrochemical analysis, surface analysis, spectroscopic techniques, and weight loss measurements. However, these methods face challenges such as replicating real-world conditions and the significant time required to observe corrosion. Additionally, interpreting data from surface analysis and electrochemical techniques can be challenging, hampering the accurate determination of corrosion mechanisms. In recent years, computational power has increased exponentially, and novel advanced computational methods have emerged to study complex processes like corrosion. These techniques have rapidly evolved across abroad areas of materials science, enabling the modelling, probing, and analysis of physicochemical reactions. Without the costly and timeconsuming nature of experiments, computational modelling serves as an excellent complement, helping to provide insight of corrosion processes and develop new theories. However, corrosion studies require relatively larger system setups and longer timescales to accurately capture the dynamics of the process. No single available modelling technique is able to cover the required size, timescale, and accuracy of the target system. As a result, multiscale modelling methods must be employed to accurately model corrosion processes. In this review, we start by introducing the general mechanisms of corrosion and briefly discussing the computational techniques applied to atomic scale corrosion mechanism studies, namely density functional theory (DFT) and molecular dynamics (MD) simulations. We then develop into fundamental studies on molecular adsorption on iron/steel surfaces, followed by reactive molecular dynamics studies (ReaxFF-MD) on the passivation and depassivation mechanisms of iron/steel under various conditions. We will further explore how these studies can contribute to the understanding of corrosion mechanism and development of corrosion inhibitors, discuss the limitations of existing techniques, and finally, outline future research directions.