The superior engineering properties and excellent biocompatibility of titanium alloy (Ti6Al4V) stimulate applications in biomedical industries. Electric discharge machining, a widely used process in advanced applications, is an attractive option that simultaneously offers machining and surface modification. In this study, a comprehensive list of roughening levels of process variables such as pulse current, pulse ON time, pulse OFF time, and polarity, along with four tool electrodes of graphite, copper, brass, and aluminum are evaluated (against two experimentation phases) using a SiC powder-mixed dielectric. The process is modeled using the adaptive neural fuzzy inference system (ANFIS) to produce surfaces with relatively low roughness. A thorough parametric, microscopical, and tribological analysis campaign is established to explore the physical science of the process. For the case of the surface generated through aluminum, a minimum friction force of ~25 N is observed compared with the other surfaces. The analysis of variance shows that the electrode material (32.65%) is found to be significant for the material removal rate, and the pulse ON time (32.15%) is found to be significant for arithmetic roughness. The increase in pulse current to 14 A shows that the roughness increased to ~4.6 µm with a 33% rise using the aluminum electrode. The increase in pulse ON time from 50 µs to 125 µs using the graphite tool resulted in a rise in roughness from ~4.5 µm to ~5.3 µm, showing a 17% rise.