This study, while in its early stages, offers a foundational exploration into using various machine learning models for predicting molecular trajectories, with a focus on aspirin. The array of models tested, including statistical sampling methods, neural networks (such as feed-forward, RNN, LSTM, GRU), Equivariant Neural Network (E3NN), Graph Convolutional Network (GCN), and diffusion models, each contributes uniquely to our understanding of molecular dynamics.