A series of coupled ZnO/SnO2 nanocomposites were prepared with different molar ratios (1:10, 1:2, 2:1, and 10:1), using a homogeneous co-precipitation method. The structural properties were evaluated by different techniques: XRD, UVDR, SEM, N-2 adsorption, and IR. The photocatalytic activity of the samples was tested with the main goal of Eosin Y degradation from wastewaters. The prepared nanocomposites/systems exhibit higher photocatalytic activity than a single semiconductor photocatalyst and ZnO can effectively improve the photocatalytic efficiency of SnO2 under UV illumination. A direct neural network modeling methodology, based on feed-forward neural networks, was performed in order to evaluate the efficiency of the photodegradation process of Eosin Y, depending of the reaction conditions. The developed model considered the following parameters with significant influence on the approached process: crystallite size, surface area, absorbtion edge, TOC values, time of reaction, and catalyst concentration as inputs and the final dye concentration as output. Accurate results were obtained in the validation phase of the neural model: relative average error under 4 % and a correlation between experimental and simulation data of 0.999.