Object pose alignment, a prerequisite for many computer vision tasks, e.g., face recognition, 3D face reconstruction, robotics, augmented reality, etc. There are lot of research to address this issue, still, there are still numerous issues regarding the problem. Among which one of them is the computational efficiency. To address this issue, this article proposes a novel method for object pose alignment of 6 DoF with Spiking Neural Network (SNN). SNNs are biologically inspired neural networks which replace traditional networks through their energy efficiency and event-driven processing mechanism. The method uses SNN to predict the translation and rotation coordinates for the base position to align with the ground truth pose. The proposed method shows potential by reducing the computational cost by almost 10% and the final results of the problem are represented in the result section, representing the initial pose and the aligned pose after training with SNN.