In big cloud data centers, the best physical machine (PM) is selected using the Virtual Machine Placement (VMP) process. To address this issue, a number of approaches have been proposed. Nevertheless, the existing solutions only take into account a small number of resource categories, which leads to an uneven load and ultimately, the activation of superfluous physical computers within the data center. The aim of this research is to maximize resource usage while lowering power use and carbon footprints by integrating a Hybrid Swarm Bipolar with Walk-Spread Algorithm with a unique Deformable Graph Convolutional Network (DGCN-SB-WSA)-aware virtual machine placement architecture. To ensGoogleent VM scheduling and management, real-world cloud workloads are analyzed using the Google Cluster Dataset (GCD). The Deformable Graph Convolutional Network (DGCN) dynamically models cloud infrastructure as a graph that captures intricate relationships among PMs and VMs, enabling adaptive placement choices. The Hybrid Swarm Bipolar with Walk-Spread Algorithm (SB-WSA) then uses a dual-phase search approach to balance local exploitation with global exploration, minimizing premature convergence and increasing performance while optimizing Virtual machine (VM) allocation. Comparing the proposed method to conventional VM placement techniques, experimental results show that it dramatically 27 KW lowers power consumption, improves 98% resource usage and 180 kg CO₂ decreases carbon emissions.