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This paper explores the numerical optimization of heat and mass transfer in the buoyancy-driven Al₂O₃-water nanofluid flow containing electrified Al₂O₃-nanoparticles adjacent to a symmetrically-vertical plane wall. The proposed model becomes a set of nonlinear problems through similarity transformations. The nonlinear problem is solved using the bvp4c method. The results of the proposed model concerning heat and mass transfer with nanoparticle electrification and buoyancy parameters are depicted in the Figures and Tables. It was revealed that the electrification of nanoparticles enhances the heat and mass transfer capabilities of the Al₂O₃ water nanoliquid. As a result, the electrification of nanoparticles could be an important mechanism to improve the transmission of heat and mass in the flow of Al₂O₃-water nanofluids. Furthermore, the numerical solutions of the nanofluid model of heat/mass transfer using the deep neural network (DNN) along with the procedure of Bayesian regularization scheme (BRS), DNN-BRS, was carried out. The DNN process is provided by taking eight and ten neurons in the first and second hidden layers along with the log-sigmoid function.