To address this challenge, we developed a unified, free, and open -source Python package called ULaMDyn, which stands for "Unsupervised Learning Analysis of Molecular Dynamics" (Figure ). It is designed to automate the discovery of hidden patterns in high-dimensional molecular datasets. ULaMDyn offers a comprehensive set of tools for preprocessing, statistical analysis, and unsupervised learning of trajectory data generated by Newton-X. Seamlessly integrated with Newton-X's surface hopping NAMD, it streamlines the processing and analysis of simulation outputs. The package also leverages dimensionality reduction and clustering techniques to enhance dataset construction for supervised learning tasks conducted using MLatom, which is similarly interfaced with Newton -X. This unified approach establishes a comprehensive pipeline that combines both supervised and unsupervised learning methodologies, thereby streamlining the analysis of molecular dynamics simulations and improving the interpretability and understanding of complex potential energy surfaces and nonadiabatic dynamics.