<p>The nonlinear telegraph equation appears in a variety of engineering and science problems. This paper presents a deep learning algorithm termed multi-term physics-informed neural networks to resolve initial boundary value problems of 2D and 3D hyperbolic nonlinear telegraph equations. The standard physics-informed neural networks include the gradients of residuals of the problem, initial conditions, and boundary conditions directly into the training process in addition to the standard PINNs loss function terms. Using multiple densely connected neural networks, termed feed-forward deep neural networks, the proposed scheme has been trained to minimize the total loss results from the multi-term loss function. Three computational examples are provided to demonstrate the efficacy and applications of our suggested method. For each illustrative example considered, we compared the proposed scheme with the standard physics-informed neural networks based on the network model train and test losses and other well-known performance measure error analysis including each problems with long-time domain. The results demonstrate that the proposed method outperforms the conventional deep learning approach physics-informed neural networks in predicting the solutions of both initial boundary value problems of telegraph equations. Therefore, the proposed computational approach can offer precise and stable solutions for long-time domain PDEs and is efficient in handling challenging nonlinear physical problems with a variety of boundary conditions.</p>