During the present experimentation, milling machining was performed on two different composites, namely carbon fiber-reinforced polymer composite and jute fiber-reinforced polymer composite, using a computer numerical control vertical machining center. The selected machining parameters were spindle speed (S), feed rate (FR), depth of cut (DOC), and flute number or cutting edge number (FN). The output parameter is the machined surface roughness (Ra). Analysis of variance was used to predict the percentage influence of each parameter on machining quality. The parameter feed rate exhibited a higher influence on the machined surface roughness, followed by spindle speed, flute number, and depth of cut in sequence. Similarly, while milling the carbon fiber composite, the feed rate had the highest influence, followed by the parameter flute number. As for the surface roughness, the feed rate had a greater effect, followed by the spindle speed. Under the same machining conditions, it was observed that the sequence of parameters influencing the jute composite and carbon composite changed in the case of cutting force generation, but the sequence of parameters was the same for both cases in terms of roughness. The outcome of the work confirmed that to achieve a smaller value of roughness in the milling of jute–epoxy composite, the optimum combination should be S = 3000 rpm, FR = 800 mm/min, DOC = 0.25 mm, and FN = 6. Similarly, to achieve the minimum surface roughness value in the milling of carbon–epoxy composite, the optimum combination of parameters should be S = 600 rpm, FR = 100 mm/min, DOC = 0.25, and FN = 6. The average roughness values obtained during the milling of jute–epoxy composite and carbon–epoxy composites are 6.685 and 3.08 μm, respectively. In this present work, it is proved that the optimum combination of parameters to get the minimum surface roughness and the amount of surface roughness produced during milling are highly influenced by the type of reinforced material. The graphs are prepared for the entire range of input parameters to identify the intermediate Ra value at any input value without the use of software.