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The model of a two-dimensional shallow water equation (so-called Saint venant 2D equation) presents the motion of water on a large lake or on the sea region. Solving this equation is to calculate the water level, the water velocities in two - directions coordinator (Oxy). This work needs mass of computations in a short time in order to forecast and control serious incidents (e.g. the dam break flow, high tidal flow in estuary, flood waves, water pollution, etc.) as soon as they happen. Up to now, PCs have been used for solving this equation but not satisfied those demands, thus the better facilities are needed. The cellular neural network (CNN) technology and the CNN Universal machines (CNN-UM) with the physical parallel computing architecture have been researched and developed making new ways for solving several types of partial differential equations. This paper introduces the application CNN in solving Saint venant equation 2D and has 5 parts: Introduction; Part 2 gives theoretical background about CNN and CNN 2D, 3D models; Part 3 analyzes and designs the problem following the CNN model with detail specifications; Part 4 identifies the boundary and initial conditions, then sets up a simulation on Matlab tools. The last gives the conclusion and evaluates the results.