<p>Understanding the connection between chemistry, structure, and ion migration in solid-state electrolytes (SSEs) is vital to enable safer, more efficient all-solid-state batteries and other advanced electrochemical devices. Atomistic simulations offer invaluable access to the intricacies of this connection. However, extracting meaningful insights often requires detailed crystallographic knowledge and painstaking examination of simulation trajectories. This work introduces a density-based unsupervised method that accurately and efficiently identifies and categorizes crystallographic sites while providing a robust framework for analyzing ionic transport from ab initio and classic molecular dynamics simulations. Unlike previous schemes, it needs no prior structural knowledge and is adaptable to various material systems. Our approach, implemented in the open-source CrySF package and validated on representative SSEs such as Li₇La₃Zr₂O₁₂ (a garnet), Li₁₀GeP₂S₁₂ (a sulfide), and Li₆PS₅Br (an argyrodite), effectively analyzes the interplay between structure, ionic mobility and collective migration phenomena, offering a powerful tool to accelerate the development of high-performance SSEs.</p>