The advent of 5G/6G broadband wireless networks brings several challenges with respect to resource allocation. In a heavily interconnected network of wireless devices, users, and their equipment, all compete for scarce resources which further emphasizes the fair and efficient allocation importance of those resources for the proper functioning of the networks. This paper tackles a crucial and timely topic, i.e., understand the various factors involved for optimizing network performance and ensuring fair access for diverse users, applications and devices. Integrating Machine Learning (ML) and Artificial Intelligence (AI) for dimensioning and mining over the network traffic can enable dynamic and intelligent resource allocation, increase network capacity, enhance the underlying capabilities between users and core network, and better correlate the Quality of Service (QoS). The scientific contribution of this paper entails novel AI models harvesting data from 5G/6G testbeds offered through the AI as a Service paradigm to enable model reuse and seamless exploitation for different learning tasks.