Password authentication persists as a crucial aspect of identity verification in today’s world due to the existing widespread use that has cultivated user familiarity and allows easy interoperability among different devices. However, users often prioritize memorability over security, opting for easily remembered passwords or common patterns, especially when adhering to company policies, which may lead to more predictable passwords. This research explores the potential of PCFGs (Probabilistic Context-Free Grammars) and Markov models to generate passwords. By doing so, our aim is to empower cybersecurity professionals, forensic analysts, and researchers with a potent toolset for assessing and fortifying password security within digital ecosystems.