In this work, we introduce an ML framework to generate long-term single-polymer dynamics by exploiting short-term trajectories from molecular dynamics (MD) simulations of homopolymer melts. Even with current advances in machine learning for MD, these polymeric materials are difficult to simulate and characterize due to prohibitive computational costs when long timescales are involved. Our method relies on a 3D neural autoregressive (NAR) model for collective variables (CVs), which enhances the Generalized Langevin Equation capabilities in modeling diffusion phenomena. ML-GLE is capable of reproducing long-term single polymer statistical properties, predicting the diffusion coefficient, and resulting in an enormous acceleration in terms of simulation time. Moreover, it is also scalable with system size.