The generation of realistic 3D microstructure images is crucial for understanding and optimizing materials in various fields, including fuel cell technology. In this article, we present a novel approach for generating synthetic 3D microstructure images using 3D Denoising Diffusion Probabilistic Models (3D DDPM). This approach extends to n-phase materials. Unlike conventional image generation techniques, our method leverages the principles of diffusion in three-dimensional space to simulate the intricate evolution of microstructures. By incorporating stochastic processes and diffusion equations, 3D DDPMs enable a more realistic and controlled representation of the dynamic processes occurring within materials. This approach generates synthetic microstructures that capture the spatial complexities inherent in real-world materials across multiple phases. Through experimental evaluation, we demonstrate that our approach generates realistic 3D microstructure images of O₂ fuel cell electrodes for two or three phases.