In this work, we introduced ULaMDyn, a robust and flexible Python package for analyzing nonadiabatic molecular dynamics (NAMD) simulations using unsupervised learning techniques. ULaMDyn integrates seamlessly with Newton-X and offers a complete pipeline for processing, reducing, and clustering high-dimensional molecular datasets, enabling researchers to uncover hidden patterns and critical molecular transitions. Through the case study of fulvene dynamics, we demonstrated its ability to identify critical geometries and provide insights into nonadiabatic transitions.