One of the most difficult scientific jobs is still identifying coagulation factor inhibitors in hemophilia. Despite being established benchmarks, the Bethesda assay and its Nijmegen version rely on intricate procedures and logarithmic interpretation, which can impede access and repeatability in environments with limited resources. In order to directly predict inhibitor titers from residual factor VIII activity, this work suggests a streamlined, computationally effective formula. AI-assisted regression was used to assess 101 hypothetical Bethesda locations. The theoretical function was fitted to both linear and fifth-degree polynomials.. The polynomial model closely matched the Bethesda reference curve (R2 ≈ 0.9999) with a mean absolute error of 0.011 BU and a maximum deviation of 0.0192 BU. These results suggest that AI-driven modeling can faithfully reproduce the Bethesda relationship and provide a straightforward, practical computational tool for clinical laboratories to improve identification and monitoring of factor VIII inhibitors.