This study investigates how artificial intelligence (AI)-driven biometric authentication systems can be made more secure using cryptographic techniques. Increasing the level of safety of biometric identification systems that are managed by artificial intelligence (AI) is the objective of this project. The aim of this work is to investigate the several ways in which cryptographic techniques can be applied to improve the security of biometric authentication systems driven by artificial intelligence (AI), especially those depending on facial recognition for authentication needs. Biometrics are increasingly used in critical sectors such as healthcare, banking, and border security; hence, these systems are vulnerable to several major risks, including sophisticated assaults, data breaches, adversarial manipulation, and repeat attacks. These are only a few of the main risks these systems run across. This research involves assessing several modern cryptographic technologies, including homomorphic encryption, cancellable biometrics, blockchain integration, and biometric template protection, in order to handle these problems. The goal of the project is to protect biometric data and respect personal information confidentiality. A prototype system designed to encrypt facial feature vectors obtained from a dataset drawn from the real world was built in Python. To get the required performance, this system used the K-Nearest Neighbor’s (KNN) classifier with Fernet symmetric encryption. The results show that encryption did not cause a reduction in model accuracy (60.87%), implying that intelligence-based recognition systems can preserve high performance and simultaneously protect data confidentiality. Cryptography was used to keep the data safe. The results of this study show that using lightweight cryptography is a good way to make biometric login systems that protect user privacy better and more scalable.