Simulation tools improve various aspects of the additive manufacturing process, however, they come with an undesirable computational time for real-world applications. Finite element analysis (FEA) that solves partial differential equations (PDE) presents promising capabilities in simple additive manufactured components as an expository problem. Yet, PDE-based solutions take significantly long CPU time due to a large number of timesteps required to simulate an additively manufactured part. With modern machine learning (ML) capabilities, a new shift towards integration of FEA and ML has been introduced, where ML algorithms emulate the behavior of the time-consuming PDE-solver for real-time analysis of PDE in a given application. In this paper, we present a deep learning (DL) model that can substitute the thermal analysis of the additive manufacturing process. The training data is obtained by sampling the established physical model’s behavior over different temperatures, cooling rates, and part’s geometries. The network architecture is composed of a Long Short-Term Memory (LSTM) to model the temporal sequence of deposition temperatures derived by PDEs. The reported R2 value on validations data is 97%, while the Mean Absolute Error (MAE) is 0.04. This paper compares the performance between the PDE and DL forecast for the thermal results. We show DL models are promising for simulation of the additive manufacturing process, and can be reliable alternatives for computationally-expensive FEM tools.