This thesis focuses on the imperative task of developing and validating an Artificial Neural Network (ANN) model for the simulation of Selective Catalytic Reduction (SCR) systems, crucial components employed to mitigate nitrogen oxide (NOx) emissions in vehicular exhaust gases. The escalating global emphasis on environmental conservation, particularly in reducing NOx emissions, necessitates innovative methodologies to comply with stringent regulatory standards. In response to this challenge, we propose a novel approach utilizing data-driven techniques, specifically neural networks, to model the intricate dynamics of SCR reactors. The modeling effort is divided into two essential categories: Thermal Modeling and Chemical Modeling. The former involves Wall Temperature and Outlet Gas Temperature ANN, while the latter encompasses Storage, NH3 Slip, and Conversion Efficiency ANN.