Miniaturized enzymatic biofuel cells (EBFCs) converting biological energy into electrical energy by using enzyme-modified electrodes are considered as a candidate to power implantable medical devices and portable electronics. In this study, modeling of the EBFC system has been conducted using finite element analysis based on COMSOL Multiphysics 4.3a in terms of cell performance, efficiency and optimum cell configurations. Two modules have been applied to obtain the maximum theoretical cell performance of on-chip microelectrodes: 1) diffusion module to incorporate the mass transport and enzymatic kinetics; 2) conductive module to integrate concentration and potential. The design rule has been developed in the steady state. The effect of orientation of the microelectrode arrays in blood artery has been studied. In the experimental part, integration of the high surface area nanomaterials such as carbon nanotubes (CNTs) and graphene could be one of the effective solutions to further improve the performance of current EBFCs. The EBFC systems employing 3D carbon micropillar arrays integrated with different nanomaterials, such as graphene, reduced graphene oxide (rGO) and rGO/CNTs composite have been investigated. The fabrication process of this micro-system combined top-down carbon microelectromechanical system (C-MEMS) technology to fabricate the 3D micropillar arrays platform and bottom-up electrophoretic deposition (EPD) to deposit various carbon based nanomaterials onto the 3D micropillar arrays. The materials characterization, electrochemical characterization and device evaluation have been conducted. Upon comparison among bioelectrodes integrated with different nanomaterials, the amperometric response of the rGO/CNTs based bioelectrodes exhibited the best electrochemical performance and the as-resulted 3D micro EBFC generated a maximum power density of 196.04 µWcm^-2 at 0.61 V. In addition, modeling of the rGO/CNTs EBFC system based on finite element analysis has also been conducted to predict the theoretical cell performance.  The power density of actual device has been noted to be 71.1% of the modeling result.