Abrasive flow machining (AFM) is gaining wide spread application finishing process on difficult to reach surfaces in aviation, automobiles, and tooling industry. A multiple regression model is proposed by using SPSS to simulate and predict the surface roughness, and material removal for different machining conditions in (AFM) on aluminum alloys. Based upon the experimental data of the effects of AFM process parameters, e.g., length of stroke, extrusion pressure, number of cycles, percentage of abrasive concentration, and abrasive grain size. The mathematical models for Ra, and material removal are established to investigate the influence of AFM parameters. Conformation test results verify the effectiveness of these models and optimal parametric combination within the considered range. The statistical model could predict about 96.1%, and 99.38% accuracy.