This work describes the development of a novel and low-cost methodology for the simultaneous quantification of five main nonsteroidal anti-inflammatory drugs (NSAIDs) in pharmaceutical samples using differential pulse voltammetry coupled with an artificial neural network model (ANN). The working electrode used as a detector was a carbon paste electrode (CPE) modified with multi-wall carbon nanotubes (MWCNT-CPE). The specific voltammetric determination of the drugs was performed by cyclic voltammetry (CV). Some characteristic anodic peaks were found at potentials of 0.337, 0.588, 0.888 V related to paracetamol diclofenac, and aspirin. For naproxen, two anodic peaks were found at 0.959 and 1.14 V and for ibuprofen an anodic peak was not observed but it did modify the baseline of the buffer at an optimum pH of 10 in 0.1 mol L^-1 Britton-Robinson buffer. Since these drugs oxidation process turned out to be irreversible and diffusion-controlled, drug quantification was carried out by differential pulse voltammetry (DPV). The Box Behnken design technique's optimal parameters were: step potential of 5.85 mV, the amplitude of 50 mV, period of 750 ms, and a pulse width of 50 ms. From the voltammetric records obtained, an ANN was built to interpret the voltammograms generated at different drug concentrations to obtain a calibration of the system. The ANN model's architecture is based on a Multilayer Perceptron Network (MLP) and a Bayesian training algorithm. The trained MLP achieves R² values greater than 0.9 for the test data to simultaneous quantification of the five drugs.