Often exploited by brute-force and credential-stuffing attacks, modern protection methods are still generally weak and simple to guess passwords. Because they are constant and ignore the context, traditional password strength meters—which rely on rule-based formulae or entropy calculations—are becoming poorer at assessing how safe a password is in the real world. The research offers a flexible, deep learning-based approach to assess password strength. The system uses convolutional, recurrent, and transformer-based neural architectures to better grasp the patterns in the syntax and meaning of passwords. To adapt to new password patterns and attack paths, the model employs massive real-world and made-up datasets, a character-level embedding approach, dynamic scoring systems, and continuous learning. The proposed model outperforms standard and baseline classifiers by a wide margin in trials in accuracy (91.6%), precision (90.2%), recall (89.7%), and ROC-AUC (0.956). Case studies reveal that the model can identify false password patterns and provide more accurate power evaluations.