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With the rapid increase in the number of Anthropogenic Space Objects (ASOs),Low Earth Orbit (LEO) is facing significant congestion, thereby posingchallenges to space operators and risking the viability of the spaceenvironment for varied uses. Current models for examining this evolution, whiledetailed, are computationally demanding. To address these issues, we propose anovel machine learning-based model, as an extension of the MIT Orbital CapacityTool (MOCAT). This advanced model is designed to accelerate the propagation ofASO density distributions, and it is trained on hundreds of simulationsgenerated by an established and accurate model of the space environmentevolution. We study how different deep learning-based solutions can potentiallybe good candidates for ASO propagation and manage the high-dimensionality ofthe data. To assess the model's capabilities, we conduct experiments in longterm forecasting scenarios (around 100 years), analyze how and why theperformance degrades over time, and discuss potential solutions to make thissolution better.

Towards a Machine Learning-Based Approach to Predict Space Object Density Distributions