Due to their strength, stiffness, and greater design flexibility, various types of composite materials and hybrid polymer composites are now used in many structures. An experimental and numerical study of the impacted hybrid composite of some laminate cases is presented in this research, and the results are reinforced by an optimization technique. Numerical models are made in sandwich from the experimental steps, with composite plies 1 serving as faces and composite plies 2 as core. The results are recorded by obtaining different levels of energy absorption capacity for the proposed composite design. The numerical model is applied to low-velocity impacts for different composite material designs and different absorbed energy levels. The study is extended to construct a sufficient range of cases for different composite designs and different absorbed energy values. The collected data is used to train a model to predict absorbed energy values for composite plate designs. The results indicate that, unlike the all-scenario cases of plate laminates, the predictive model using artificial neural network shows high performance with different scenarios, and the proposed model presents a refutation situation in anticipating peak displacement and peak load values for several types of composite material designs.