Simulating left atrial electrophysiology is crucial for advancing the diagnosis and treatment of heart diseases. The traditional modeling and simulation approach involves coupling a monodomain equation, which describes the propagation of the transmembrane potential throughout the left atrium, with the Courtemanche-Ramirez-Nattel model, which characterizes atrial electrophysiology at the cellular scale. High-fidelity discretizations, utilizing methods such as the Galerkin Finite Element method and a Backward Difference Formula scheme, demand substantial computational resources, particularly when multiple solutions are needed for varying key parameters of the problem. This makes the full-order model not applicable to clinical scenarios that require real-time and multiple simulations. To reach an efficient solution of the parametric problem, this thesis employs a data-driven approach, called Latent Dynamics Network (LDNet).