The transition to 6G networks heralds a transformative era in wireless communication, characterized by unprecedented demands for ultra-low latency, massive connectivity, and enhanced energy efficiency. Traditional Radio Access Network (RAN) architectures face significant challenges in adapting to these requirements, particularly in supporting the diverse and dynamic nature of 6G use cases such as ultra-reliable low-latency communication (uRLLC), enhanced mobile broadband (eMBB), and massive machine-type communication (mMTC). This paper addresses these challenges by proposing an intelligent RAN (i-RAN) framework that seamlessly integrates Software-Defined Networking (SDN), Artificial Intelligence (AI), Multi-Access Edge Computing (MEC), and RAN Intelligent Controller (RIC) technologies. The framework introduces dynamic network slicing, AI-driven resource optimization, and adaptive orchestration mechanisms to enable scalable, flexible, and autonomous RAN operations. Through a robust analytical model and comprehensive simulations, the proposed approach demonstrates significant improvements in Quality of Service (QoS), energy efficiency, and latency reduction, highlighting its ability to meet the stringent and diverse performance requirements of next-generation 6G networks. This work contributes to the evolution of RAN architectures by offering a scalable and intelligent solution that bridges the gap between current network capabilities and future 6G aspirations.