significant reduction in the number of training points, the training process with 8730 epochs takes only 2 h on 16 cores of Intel(R) Xeon(R) Gold 6226R @ 2.90GHZ CPUs. In summary, we have proposed a one-shot trajectory learning (OSTL) approach which allows us to extremely fast propagate quantum dynamics. The multi-time-steps output feature of our approach avoids invoking the entire neural network architecture for each time-step, thus significantly speeding-up quantum dynamics propagation. The proposed approach is non-recursive, hence also avoiding the problem of error accumulation and eliminating the need of any short-time trajectory as an input similarly to AI-QD. In contrast to AI-QD, OSTL significantly decreases the number of points (labelled data) used for supervised learning, thus making training much more efficient, which is especially useful in the case of large systems where data can be very large. As any supervised ML approach, OSTL requires a generation of the reference data to be trained on, but once trained, it can be exploited for fast propagation in massive quantum dynamics simulations, i.e., by interpolating in the simulation parameter space as shown in Fig. of the Supporting Information and finding the desired parameters. Because our OSTL approach is neither recursive nor includes time in the input, it cannot predict dynamics beyond the maximum run time of the training trajectories. Such predictions are not the design target of OSTL, while longer-time propagation from shorter trajectories is the design target of the recursive ML approaches. Hence, both OSTL and recursive ML should be considered complementary approaches and they can be combined when both massive simulations and longer-time propagation are required. Finally, although this study is dedicated to presenting a new methodology, future studies may focus on using more accurate reference approaches than an approximate method LTLME to generate data for training.