<p>Quick Response codes are widely used in many sectors for digital payments, product identification and verification, and secure communication. QR codes are easily manipulated and counterfeited despite their convenience, creating significant risks to data integrity and user security. To improve the security and dependability of QR codes, this article investigates sophisticated anti-tampering approaches. The proposed method combines machine learning approaches, physical security measures, and cryptographic algorithms to enhance the security of the QR codes from unauthorised use. The embedded information within it is secured by cryptographic techniques, and physical elements like holograms and microtext add another degree of security i.e. physical signature. Real-time anomaly detection is achieved through the use of machine learning algorithms to detect tampering. The overall protection of these combined procedures is demonstrated by experimental findings, which also indicate a notable improvement in the dependability of authentication and data security. The proposed QR code can be used in fields including access control, sensitive environments, logistics, digital payments, Direct Wi-Fi and networking and other leading applications.</p>