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The upcoming 6G systems will represent a complete paradigm shift for global communications, claiming a three-dimensional (3D) ecosystem with terrestrial and non-terrestrial networks (TNs-NTNs) to improve coverage and capacity and enable advanced applications with strict quality of service (QoS) and quality of experience (QoE) requirements. Addressing the critical research verticals toward the envisioned 2030 will require a seamless unicast/multicast/broadcast convergence and a native softwarized, disaggregated, and intelligent Radio Access Network (RAN) conception. In such a context, the network slicing paradigm is an appealing feature for enhanced differentiated traffic management. This research aims to efficiently manage radio access selection and slice allocation based on Machine Learning (ML) techniques inserted in the O-RAN framework. The project focuses on finding the best combination of …