AISI 52100 is a high chromium-carbon alloy steel having excellent compressive strength, wear resistance, hardness and toughness characteristics making it suitable candidate for automotive and aerospace applications. The finishing of such hardened alloy steel products is difficult using conventional finishing process that may hamper surface integrity and thus the sustainability of the component may be hindered. To this end, an unconventional finishing technology such as Magnetic abrasive finishing (MAF) could be utilized as a promising alternative for obtaining precise and sustainable finishing among versatile products from micro to nano ranges. In this experimental work, the Box-Behnken design of response surface methodology (RSM) was utilized fordesigning and conducting fifteen finishing experiments considering three MAF parameters namely voltage, tool rotational speed per minute and finishing time. The response variables considered as performance characteristics i.e., surface roughness and material removal rate (MRR), were measured after performing experiments on chemically treated AISI 52100 steel, at different combinations of input parameters. The oxidizing agent utilized was hydrogen peroxide for workpiece chemical treatment. The chemically treated alloy steel gets softened and passive films are generated, which was efficiently finished using magnetic processing force. The response surface plots were utilized to determine the influence of input MAF process parameters on the responses of surface roughness and MRR. The ANOVA analysis was performed for determining the most influential parameter and model significance. The desirability-based approach was used for multi-objective optimization and optimal set of parameters are realized, thus providing a sustainable finishing environment. Finally, an adaptive neuro fuzzy inference system (ANFIS) analysis was also performed for validating the results and establishing the relationship between input variables and output. The comparison of RSM and ANFIS resultsconcludes superiority of ANFIS model in predicting the performance characteristics of MAF process in finishing AISI 52100 alloy steel.