The emergence of 6G networks heralds an era of unprecedented connectivity, speed, and complexity, paving the way for revolutionary advancements such as holographic communications, autonomous systems, and the Internet of Everything (IoE). However, with these enhanced capabilities comes a host of critical security challenges, including sophisticated cyber threats, privacy vulnerabilities, and the protection of billions of interconnected devices. Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the security landscape of 6G networks, emerging as powerful catalysts for transformation. These technologies enable proactive threat detection, adaptive defense mechanisms, and real-time response strategies, fostering a resilient and intelligent security framework. AI-driven models can dynamically detect anomalies, autonomously refine protective measures, and facilitate self-optimizing security operations, ensuring a robust and future-ready defense system. Meanwhile, ML techniques facilitate predictive analytics, continuous learning, and advanced encryption strategies, tailored to the ever-changing threat environment. Furthermore, AI plays a pivotal role in securing network slices, IoT ecosystems, and edge infrastructures, offering robust and scalable protection within a highly virtualized and decentralized network architecture [1,3]. Key innovations such as federated learning, behavioural analytics, and post-quantum cryptography serve as critical enablers for enhancing privacy, resilience, and trust in 6G environments. Despite their vast potential, challenges such as model robustness, explainability, and seamless integration with legacy systems must be addressed to fully harness the power of AI and ML in securing next-generation networks. By providing a comprehensive exploration of AI and ML-driven security solutions, this study aims to foster trust, inspire innovation, and lay the foundation for secure and reliable 6G adoption worldwide [14,15]. Problem Statement: As 6G networks evolve, they introduce new security challenges due to their complex architecture, massive connectivity, and ultra-low latency requirements. Traditional security mechanisms are inadequate to combat advanced cyber threats, including AI-powered attacks and vulnerabilities arising from edge computing and decentralized networks. Furthermore, the advent of quantum computing threatens existing cryptographic standards, rendering them obsolete and exposing 6G networks to unprecedented security risks. Therefore, there is a critical need to leverage AI, ML, and quantum-resistant security solutions to enhance threat detection, automate response mechanisms, and develop resilient cryptographic techniques to secure 6G communications against both classical and quantum cyber threats.