Machine learning algorithms are used to ameliorate the thermodynamic properties of Fe-Cr alloys with various elements, such as Ni, Mo, Al, W, V, and Nb. It is used to optimize the composition of the alloys, resulting in the prediction of new, stable alloys with improved mechanical properties. Fe-Cr-based alloys are used in industrial and nuclear applications. However, to meet the increasing demands for materials performance, multi-component alloying is required. Raising the number of alloy components could cause a notable rise in the intricacy of experimental materials design, resulting in longer development periods and elevated expenses. The stability of Fe-Cr-based steels is a crucial factor for construction materials, but it is complicated due to the binary Fe-Cr phase diagram, which shows that alloys can decompose into Fe and Cr rich phases at low and medium temperatures. Density functional theory (DFT) studies indicate that Fe-Cr alloys with a low concentration of 6-8 percent Cr exhibit an unusual thermodynamic stability with a negative mixing enthalpy. This is due to antiferromagnetic exchange interactions between Fe-Cr pairs in the first nearest neighborhood, causing the magnetic moments of Cr atoms to align antiparallel to the Fe moments. The same phenomenon is observed between Cr-Cr pairs in the first neighborhood, leading to frustration in the local magnetic configuration and contributing to the negative mixing enthalpy as the concentration of Cr increases. In addition, the lattice parameters and calculated bulk modulus of Fe-Cr alloys show abnormal behavior at low-Cr concentrations, with the lattice parameter experiencing a maximum change in the range of 0-8 percent Cr, while the bulk modulus has a minimum. The use of data-driven methods for predicting the properties of new Fe-Cr-based alloys has been limited, despite the numerous firstprinciples simulations available. However, a recent study utilized the exact muffin-tin orbitals (EMTO) method and the coherent potential approximation (CPA) to investigate the effect of multicomponent alloying with Ni, Mo, Al,and W on the thermodynamic and mechanical properties of Fe-Cr-based bcc alloys. The study aimed to create a database for designing materials for nuclear energy applications and found a correlation between the mixing enthalpy and the local moment on Fe atoms in the alloys. The results indicated that elements that reduce the Fe magnetic moment increase the stability of alloys. Among the studied elements, Al was predicted to have the most significant stabilizing effect on binary Fe-Cr alloys, while nickel was found to have a destabilizing effect on the solid solutions. The use of 1 percent by weight (1 percent wt) of Nb, W, or Mo is found to decrease the alloys' stability, while Cr is determined to have the highest contribution to the elastic modulus based on elastic property calculations. However, when the Cr content exceeds approximately 9%, the mixing enthalpy becomes positive. In contrast, adding Al to Fe-Cr alloys is observed to increase the elastic moduli, while Ni has the opposite effect.