Milling process is a common machining operation that is used in the manufacturing of complex surfaces. Machining-induced residual stresses (RS) have a great impact on the performance of machined components and the surface quality in face milling operations with parameter cutting. The properties of engineering material as well as structural components, specifically fatigue life, deformation, impact resistance, corrosion resistance, and brittle fracture, can all be significantly influenced by residual stresses. Accordingly, controlling the distribution of residual stresses is indeed important to protect the piece and avoid failure. Most of the previous works inspected the material properties, tool parameters, or cutting parameters, but few of them provided the distribution of RS in a direct and singular way. This work focuses on studying and optimizing the effect of cutting speed, feed rate, and depth of cut for 6061-T3 aluminum alloy on the RS of the surface. The optimum values of geometry parameters have been found by using the L27 orthogonal array. Analysis and simulation of RS by using an artificial neural network (ANN) were carried out to predict the RS behavior due to changing machining process parameters. Using ANN to predict the behavior of RS due to changing machining process parameters is presented as a promising method. The milling process produces more RS at high cutting speed, roughly intermediate feed rate, and deeper cut, according to the results. The best residual stress obtained from ANN is ‒135.204 N/mm2 at a cutting depth of 5 mm, feed rate of 0.25 mm/rev and cutting speed of 1,000 rpm. ANN can be considered a powerful tool for estimating residual stress