Anaerobic digestion (AD) is a well-established organic waste management process due to its high renewable energy yield, material recovery, cost-effectiveness, and small environmental footprint. However, AD systems still have some limitations that must be overcome to maximize the benefits. Numerous advancements have been proposed to mitigate the effect of these limitations, such as pretreatment, co-digestion, and most recently, microbial electrolysis cells (MECs). In such bio-electrochemical reactors, exo-electrogenic bacteria are stimulated by a constant supplied voltage. The reviewed literature agrees that combining AD with MEC improves organics fermentation and methane generation multiple studies covered different areas of the performance of MEC under various conditions. No study has optimized the organic composition of waste in MECs in terms of methane production and organic treatments at different voltages. Therefore, this thesis aims to develop a statistical model for predicting and optimizing methane production based on the organic composition of different substrates during the MEC process. To understand the MEC mechanism on the digestion of lipids, carbohydrates, and proteins (LCP), various mixtures of plant oil, cooked rice, and ground beef were investigated as LCP sources. Furthermore, a design of experiment by central composite design-response surface methodology (RSM) was applied, and a set of batch MEC assays were operated under mesophilic conditions. Moreover, the RSM was used to assess the effects of four main variables (lipids, carbohydrates, proteins, and voltage) during digestion. The model was validated by applying organic composition concentrations of different substrates compiled from the literature. Experimental results confirmed that the supplied voltage could improve methane yield by 23% to 89% with the applied voltage of 1.5 V compared to the conventional AD at high concentrations of lipids and carbohydrates. The highest methane yield was achieved at an LCP ratio of 20:70:10 and a suppli