  Network operators are facing new challenges when meeting the needs of their
customers. The challenges arise due to the rise of new services, such as HD
video streaming, IoT, autonomous driving, etc., and the exponential growth of
network traffic. In this context, 5G and B5G networks have been evolving to
accommodate a wide range of applications and use cases. Additionally, this
evolution brings new features, like the ability to create multiple end-to-end
isolated virtual networks using network slicing. Nevertheless, to ensure the
quality of service, operators must maintain and optimize their networks in
accordance with the key performance indicators (KPIs) and the slice
service-level agreements (SLAs).
  In this paper, we introduce a machine learning (ML) model used to estimate
throughput in 5G and B5G networks with end-to-end (E2E) network slices. Then,
we combine the predicted throughput with the current network state to derive an
estimate of other network KPIs, which can be used to further improve service
assurance. To assess the efficiency of our solution, a performance metric was
proposed. Numerical evaluations demonstrate that our KPI prediction model
outperforms those derived from other methods with the same or nearly the same
computational time.
