<p>Cloud computing has generated a huge amount of data that puts enormous pressure on internet infrastructure. Companies are therefore struggling to find solutions to alleviate this pressure and solve the data problem. The concept of virtual machine (VM) migration and datacenter architecture for the cloud have a significant impact on latency and energy costs. Optimizing VM migration and dynamic resource allocation using advanced technical tools, such as Bayesian neural networks (BNNs), has therefore become a crucial issue for the cloud industry. This work focuses on using the dynamic Bayesian neural network (DBNN) approach to stabilize physical machine (PM) utilization to reduce energy consumption and latency. The novel MVMM modeling approach for VM migration presents statistical modeling and analysis via a DBNN study to determine where and when a specific virtual machine migrates, this is achieved by taking into account probabilistic dependencies between datacenter parameters. In order to evaluate the suggested approach, we integrated the MVMM scheduler into the GreenCloud simulator, while considering fundamental datacenter characteristics, such as scheduling mechanism, network architecture, connection, load, and inter-VM communication. Performance results demonstrate that the MVMM approach saves up to 35% energy compared to other scheduling algorithms.</p>