The Atlantic Meridional Overturning Circulation (AMOC) is an important tipping element of the climate system, with the potential to undergo an abrupt transition from its present strong state to a weak state. Such a collapse would have severe global consequences, including regional cooling, sea-level rise, altered precipitation patterns, and cascading impacts on other climate tipping elements. Both statistical and physics-based early warning signals (EWS) of an approaching AMOC tipping event have been proposed. Here, we introduce a convolutional neural network (CNN)–based framework designed to predict the distance of an AMOC state to its tipping point under imposed freshwater flux forcing. We first evaluate the CNN model using simulations from the Earth System Model of Intermediate Complexity CLIMBER-X. We then test its generalization capabilities by applying the CNN model, trained on CLIMBER-X data, to the AMOC tipping trajectory obtained recently in the Community Earth System Model (CESM). Explainable AI methods are used to identify the spatiotemporal features most relevant to the predictions. Our results demonstrate the potential of deep learning to provide reliable estimates of the distance to the AMOC tipping point and generalize across models of varying complexity.