Influence of organic coating on the giant magneto impedance effect was experimentally investigated in Zn complex-coated dielectric Fe-based amorphous wires and optimized the giant magneto impedance (GMI) effect using artificial neural networks and MATLAB. A three-node input layer, a one-node output layer and three hidden layers with 21 neurons and full connectivity between nodes were developed with the transfer functions hyperbolic tangent in hidden layers and sigmoid in output layer. The input parameters were frequency, static magnetic field and sample type, while the output parameter was the giant magneto impedance effect. When the network performance was tested using untrained sample data, the average correlation and prediction error of giant magneto impedance effect were found to be 99 and 0.4 %. An analytical equation as depending on experimental data has been determined by using MATLAB Curve Fitting Toolbox (TM) for giant magneto impedance. The square of the correlation and the root meansquared error were found to be 99 % and 0.89 respectively. The models including the different kinds of samples prepared have a good prediction capability and agreement with experimental results.