Graphene oxide displays the great capability for cement composites and the complexity for the observation of nanomaterials in the scanning electron microscope (SEM) images. This paper proposes an intelligent model “improved Zhang network–based generalized particle swarm (IZN-GPS) algorithm” to analyze the microstructure characteristics of graphene oxide–reinforced cement composite. At first, the cement mortar was prepared by adding graphene oxide, ordinary Portland cement, water/cement ratio, and fine aggregates. The cement composite prepared is then investigated by the proposed algorithm using SEM images. These images are binarized using Otsu’s approach that displays the pores and solid materials in the cement composite as white and black colors, respectively. Finally, the proposed IZN-GPS algorithm accurately determines the microstructures of GO-reinforced OPC composite by extracting required information from the binary images. The training error generated in the improved Zhang network is minimized using generalized particle swarm (GPS) algorithm. As a result, the proposed IZN-GPS algorithm effectively analyzes the microstructures and indicates that the GO-reinforced OPC composite has fewer pore regions and exhibits high durability and strength properties. The performance of the proposed IZN-GPS algorithm is examined using different measures, namely durability, tensile strength, compressive strength, porosity, flexural strength, cost, and accuracy. The experimental results inherit that the proposed IZN-GPS algorithm using GO-reinforced OPC composite achieves greater accuracy, enhanced compressive strength, tensile strength, and durability with less porosity rate as compared to other techniques.