As Fifth Generation Networks (5G) networks evolve, operators have an opportunity to create high-quality customer experiences by ensuring seamless and personalized connectivity through consumer-oriented network slicing. However, effectively managing consumer slices remains a challenge due to dynamic user demands, mobility patterns, and the need for real-time Quality of Service (QoS) assurance. This paper presents an Artificial Intelligence (AI)-driven framework for intelligent service feasibility, qualification, and provisioning in 5G networks, to automate consumer slicing. By leveraging real-time data from the Radio Access Network (RAN) and Core Network (CN), along with Service Quality Indicator (SQI), the framework enables dynamic resource allocation, predictive service qualification, and proactive slice optimization. Key innovations of the frame-work include the coherent integration of AI-driven slice load prediction, mobility-aware service provisioning, and automated QoS assurance via Application Programming Interfaces (APIs), ensuring optimal performance for consumer applications while maintaining compliance with Service Level Agreement (SLA). Experimental results demonstrate the framework’s capabilities in enhancing user experiences by enabling personalized slice selection, reducing service disruptions, and optimizing network resource utilization.