Empirical formulas were derived for the interface shape, mechanical properties, and electrical characteristics of accumulative roll bonded (ARB) Cu/Nb laminated materials, based on relevant literature data. These formulas were incorporated into a forward analysis model using finite element analysis, enabling the calculation of yield stress and conductivity from the spatial distribution of Cu/Nb two phases. By randomly varying the layer thickness and interface shape in the two-phase spatial distribution and conducting repeated forward analyses, a database linking microstructural descriptors with yield stress and conductivity was created. These microstructural descriptors include volume fraction, geometric features, topological features, spatial correlation functions, and persistent homology. The significance of each microstructural descriptor on yield stress and conductivity was quantified using machine learning techniques. The results revealed that the Cu volume fraction, layer thickness, and 0th Betti number are crucial for yield stress, while for conductivity, the Cu volume fraction has the strongest influence, followed by layer thickness and layer continuity. Based on these outcomes, the Pareto front for ARB Cu/Nb laminates in the strength-conductivity space was presented.