Boosted by the Internet of Things (IoT), future cellular communication networks will be able to support new services with high social impact. Corresponding applications are expected to span almost every domain, especially the most critical ones, such as the medical field or smart factories. However, their adoption is conditioned by strict compliance with stringent quality of service (QoS) requirements for the underlying network. In particular, the achieved throughput and latency are of utmost importance for efficient monitoring with multiple sensors in a variety of application areas. In this paper, we propose an intelligent self-configurable system structure, achieved through resource allocation techniques using machine learning, and capable of flexibly fulfilling such requirements. In addition, special care was taken to minimize the transmit power of the IoT devices while efficiently supporting different application-dependent traffic types, an essential prerequisite for any wide adoption of such systems with massive network access.