Roller burnishing is a prominent solution for machining hardened steels and most investigations focused on improving the burnished quality. However, the impacts of process parameters on the energy efficiency (EF) under the minimum quantity lubrication condition (MQL) have not been considered. The purpose of this investigation is to analyze the impacts of burnishing factors, including the burnishing speed (S), depth of penetration (D), the air pressure (P), and the flow rate (Q) on the EF of the minimum quantity lubrication-assisted internal roller burnishing (MQLAIB) process. The EF model of were proposed with the aid of the adaptive neuro-based-fuzzy inference system (ANFIS). The results indicated that the S was found to be the most effective factor, followed by the D, Q, and P, respectively. The developed EF model could be applied to forecast the response values for the MQLAIB process.