This paper presents a novel methodology for mod-eling memristive biosensing within COMSOL Multiphysics, fo-cusing on critical performance metrics such as antigen-antibody binding concentration and output resistive states. By studying the impact of increasing inlet concentrations, insights into binding concentration curve and output resistance variations are uncov-ered. The resultant simulation data effectively trains a support vector machine classifier (SVMC), achieving a remarkable accu-racy rate of 97%. The incorporation of artificial intelligence (AI) through SVM demonstrates promising strides in advancing AI-based memristive biosensing modeling, potentially elevating their performance standards and applicability across diverse domains.