This work presents an application of artificial intelligence tools to search for approximate explicit equations to evaluate the effectiveness factor of catalytic particles containing immobilized enzymes under isothermal and stationary conditions, Michaelis-Menten kinetics, and slab particle format. In these conditions, the mass balance within the particle is represented by a boundary-value problem that can be solved by using the orthogonal collocation method on finite elements, a highly demanding computational procedure. Therefore, use of symbolic regression, an artificial intelligence tool, to develop approximate explicit equations becomes very attractive. The input data used to perform symbolic regression were simulated using the orthogonal collocation method on finite elements. Using this tool made it possible to develop an explicit equation capable of evaluating the effectiveness factor with a maximum absolute percentage error of 2.51%.