We are becoming more and more dependent on digital communication, which makes protecting data from cyber threats a top priority. Threats from quantum computing, complex cyberattacks, and weaknesses in IoT devices are only a few examples of the modern security issues that traditional cryptographic models and network security protocols frequently fail to handle [1, 2]. Cybercriminals are using vulnerabilities in antiquated security systems more frequently as technology develops, which makes the need for creative defense tactics to grow. The Hybrid Adaptive Security Model (HASM) proposed in this study integrates cutting-edge cryptographic innovations such blockchain-based authentication methods, artificial intelligence (AI)-driven anomaly detection, and quantum-resistant encryption [3, 4]. HASM provides a multi-layered, dynamic defense strategy that adjusts to threats in real time while preserving system confidentiality and integrity. Post-quantum cryptography (PQR) algorithms like New Hope, machine learning methods like Isolation Forest for anomaly detection, and smart contracts for decentralized identity verification are some of the elements of this architecture that we develop and assess. When compared to traditional systems, the testing results demonstrate a significant improvement in threat detection accuracy (96%), increased encryption strength, and decreased susceptibility in authentication processes.