This study explores how predictive modeling can be used to assess and improve the corrosion resistance of steels by modifying metal forming parameters. Traditional statistical methods often fall short when evaluating corrosion resistance due to the stochastic nature of structural transformations during plastic deformation. To address this, we applied Fuzzy Logic within the MATLAB environment to uncover the non-linear dynamics between hot rolling conditions, microstructure, and corrosion rates. Drawing on experimental data from AISI 321 and S32750 steels, we constructed Mamdani-based mathematical models to visualize how deformation levels, pass counts, and microalloying affect intergranular and pitting corrosion. Subsequent validation of the models revealed a key finding: high predictive accuracy requires more than just processing parameters and basic structural data. It is critically important to include mechanical properties, namely yield strength and hardness, in the input dataset, since a material's resistance to corrosion-driven degradation is fundamentally tied to its stress-strain state.