Intelligent 5G Evolution: AI-Augmented Hybrid Model with Network Slicing and Autonomous Self-Healing for Future-Ready Connectivity

The increasing demand for dynamic, efficient, and resilient network management in 5G systems has driven advancements in AI-enhanced architectures. This research proposes an AI-Augmented Hybrid 5G Model that integrates dynamic network slicing, self-healing capabilities, and AI-driven predictive analytics to optimize network performance and reliability. The hybrid model employs supervised and unsupervised machine learning algorithms, achieving a fault detection accuracy of 95.2% and traffic demand prediction accuracy of 92.8%. Key innovations include dynamic resource allocation for enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communication (mMTC). Self-healing capabilities reduced fault recovery times to an average of 300 ms, significantly improving system uptime to 99.8%. Compared to traditional 5G systems, the model demonstrates a 50% reduction in latency, a 28.6% improvement in resource efficiency, and an 80% faster slice reconfiguration. These results highlight the model’s potential to revolutionize 5G network management, ensuring reliability and adaptability for diverse applications. The study concludes by exploring the applicability of the hybrid model in future 6G networks and cross-domain orchestration.