This project presents a novel traffic steering solution for Open RAN (ORAN) architectures, addressing the limitations of current proprietary and inflexible 5G systems. The work proposes a layered optimization framework that jointly addresses flow-split distribution, congestion control, and scheduling, using a network utility maximization approach. The solution is split into long-term and short-term sub-problems mapped to ORAN-native applications (rAPPs and xAPPs), integrating service-level agreements (SLAs) to manage congestion adaptively. The report also explores the impact of incomplete traffic demand information on traffic steering decisions and system performance. This work contributes to the growing body of research aimed at realizing intelligent, modular, and vendor-agnostic RAN deployments. Future directions include leveraging machine learning for predictive optimization and user-centric traffic control.