The quaternaries $$In_{1 - x} Ga_{x} As_{y} P_{1 - y}$$ I n 1 - x G a x A s y P 1 - y are the main promising elements for the fabrication of optoelectronic devices. The adjustment of their physical parameters is assumed by the change of the molar fraction $$x$$ x and $$y$$ y . These parameters can be affected by the variation of temperature and pressure. To make the theoretical diagnosis of these materials, it is fundamental to know the energy gap ‘$$\varvec{E}_{\varvec{g}}$$ E g ’ and the lattice parameter ‘$$a$$ a ’, over a wide range of chemical compositions $$0 \le x \le 0.47$$ 0 ≤ x ≤ 0.47 and $$0 \le y \le 1$$ 0 ≤ y ≤ 1 , at different temperatures and pressures. We show that by using the Artificial Neural Network method optimized by the Levenberg Maquardt algorithm ANN-LM, it is possible to obtain results very close to the experiment. The scatter plot and error calculation show that the ANN-LM model provides more accurate values of the lattice parameter than those calculated by Vegard’s law. On the other hand, the energy gap values $$Eg (x, y, T)$$ E g ( x , y , T ) estimated, using the ANN-LM model, proved to be close to the experimental values that those calculated by the empirical equations. In addition, the ANN-LM method allowed us to estimate with great accuracy the values of the energy gap at different temperatures and pressures $$Eg (P, T)$$ E g ( P , T ) . Our work provides crucial information on the physical properties of the quaternary without the use of approximations, and without taking into account the hypothesis of a perfect agreement between $$InGaAsP$$ InGaAsP and $$InP$$ InP substrate.