The tomato is an annual herbaceous plant, of the Solanaceae family. It is cultivated for its fruits which are consumed either fresh or cooked, or processed industrially. Its growth is a complex phenomenon which involves several parameters. A study of the growth parameters carried out in the region of Daloa (Côte d’Ivoire) showed a complexity of the growth of the tomato at the level of the number of leaves, the length of the leaves, the width of the leaves, the height of the trunk and the circumference of the trunk of the tomato plant. For this purpose, mathematical models were developed to predict the growth of the tomato plant from artificial neural networks for the number of leaves, the length of the leaves, the width of the leaves, the height of the plant and the circumference of the trunk of the tomato plant. The coefficients of determination between the experimental measurements and the measurements predicted by artificial neural networks are respectively 0.9722; 0.9925; 0.997; 0.9945 and 0.9926 for plant height, number of leaves, plant circumference, leaf length and width. These results are satisfactory insofar as all the coefficients of determination (R2) are greater than 0.97. These coefficients close to 1 show a good interpolation between the experimental values ​​and those predicted by the model. They indicate that the values ​​predicted by artificial neural networks are almost more than 97% close to the experimental values. Because of this, artificial neural networks are reliable enough to predict tomato growth in leaf count, leaf length, leaf width, plant height, and trunk circumference of the tomato plant.