Nowadays, the battery is the primary power source for electrifying the transition of the transport sector and bridging the gap in renewable energy intermittency. Furthermore, optimizing the electrochemical performance of the battery prevents its chemical aging, which can be verified by tuning the kinetic and diffusion parameters of the electrodes and electrolyte/electrode interface. This work focuses on predicting the diffusion parameters of metal batteries that are currently not experimentally realized in laboratory conditions. First, diffusion equations are used to analyze the relation between the diffusion coefficient and Warburg factor for the monovalent and multivalent metal ion batteries to predict the theoretical values of the diffusion coefficient at different temperatures. Second, the relationship between the charge transfer resistance and the Warburg factor is modeled to predict speculative behavior and calculate the fitting parameters. Finally, the modeled Randles‐Sevcik equation indicated the relationship between peak current and the scan rate at different diffusion coefficients. Compared to the existing algorithms available for battery modeling, this research is the first of its kind.