Molecularly imprinted polymers (MIPs), often called “synthetic antibodies”, are highly attractive as artificial receptors with tailored biomolecular recognition to construct biosensors. Electropolymerization is a fast and facile method to directly synthesize MIP sensing elements in situ on the working electrode, enabling ultra-low-cost and easy-to-manufacture electrochemical biosensors. However, due to the high dimensional design space of electropolymerized MIPs (e-MIPs), the development of e-MIPs is challenging and lengthy based on trial and error without proper guidelines. Leveraging machine learning techniques in building the quantitative relationship between synthesis parameters and corresponding sensing performance, e-MIPs’ development and optimization can be facilitated. We herein demonstrate a case study on the synthesis of cortisol-imprinted polypyrrole for cortisol detection, where e-MIPs are fabricated with 72 sets of synthesis parameters with replicates. Their sensing performances are measured using a 12-channel potentiostat to construct the subsequent data-driven framework. The Gaussian process (GP) is employed as the mainstay of the integrated framework, which can account for various uncertainties in the synthesis and measurements. The Sobol index-based global sensitivity is then performed upon the GP surrogate model to elucidate the impact of e-MIPs’ synthesis parameters on sensing performance and interrelations among parameters. Based on the prediction of the established GP model and local sensitivity analysis, synthesis parameters are optimized and validated by experiment, which leads to remarkable sensing performance enhancement (1.5-fold increase in sensitivity). The proposed framework is novel in biosensor development, which is expandable and also generally applicable to the development of other sensing materials.