Network slicing is an integral part of 5G, which supports next-generation wireless applications over a shared network infrastructure. It paves the way to leverage the full potential of 5G by increasing the efficiencies through differentiation and faster time-to-market. In this work, we propose a Mobile Virtual Network Operator (MVNO) Slice Resource Allocation Architecture (MSRAA) for supporting different network slices in the 5G data plane. MSRAA supports QoS parameters, including Guaranteed Bit Rate (GBR) and Maximum Delay Budget. Using long short-term memory (LSTM) neural networks, we predict network slices bandwidth requirements for efficiently allocating the resources. To reduce revenue loss to the network operators due to forecasting errors, the proposed Bandwidth Admission Control (BAC) algorithm, reallocates resources from lower priority slices (e.g., best-effort users) to higher priority slices (e.g., guaranteed service users). Using Mondrain Random Forests in our Delay Admission Control (DAC) algorithm, we predict the end-to-end delay and admit flows into slices that can satisfy delay requirements. We implement MSRAA on our advanced 5G Core testbed and evaluate User Service Request (USR) acceptances and do a complete cost-benefit analysis of our architecture. We show that for eMBB-GBR and eMBB-Non-GBR slices, our algorithm is showing a significant reduction in costs. © 2020 IEEE.