The applications of Neutrosophic theory are reflected in various spheres of real life. The comprehensive and flexible nature of these neutrosophic sets makes the researchers to develop neutrosophic decision-making models. This research work proposes neutrosophic based deep learning decision model to determine the optimal match between the players and the games. The objective of this proposed model is to integrate neutrosophy with Deep learning algorithm and to apply the same in matching the player’s skill sets with the suitable games. This model will certainly facilitate in grouping the players for a specific game based on their input physical traits which are considered as the model features. The multi-criteria decision-making approach of Level Based Weighting Assessment (LBWA) is used in feature reduction. The efficacy of this model is measured using the performance metric values and it is observed to be both time and energy efficient. This model shall be applied to other similar decision-making circumstances and this model shall be extended with other methods of feature reduction to take up comparative studies.