This study introduces the rec-GLI function, a recursive function, aimed at accurately fitting cyclic voltammograms (CVs) under Nernstian conditions and estimating key parameters on electroactive monolayers or redox-responsive materials. The rec-GLI function is derived from the GLI model and improves upon previous functions by incorporating mathematical recursion and curve fitting algorithms implemented in Python and MATLAB. A comparative analysis is conducted between the rec-GLI function and the GLI function, demonstrating the superior accuracy of the former in fitting CVs and estimating key parameters such as peak potential, peak intensity, full width at half maximum, surface coverage, and lateral interactions. The rec-GLI function proves particularly effective in cases where the peaks exhibit narrow widths and exhibits agreement with experimental data.