The multiPhysicsFoam framework provides a flexible basis for region-by-region coupling of multiphysics problems in OpenFOAM. Within this framework, each region is described by a modular set of governing partial differential equations, while appropriate jump and transmission conditions are imposed at region interfaces. For volumetrically coupled physics, multiPhysicsFoam currently relies on segregated or interface-coupled solution strategies, while also supporting monolithic interface coupling for conjugate heat transfer. Originating from developments in foam-extend, the framework has been successfully applied to a wide range of multi-region problems, including conjugate heat transfer, multiphase flows, fluid–structure interaction, and fuel cell simulations. In interface-coupled approaches, physical variables such as velocity, pressure, and temperature are often coupled explicitly or semi-implicitly within regions and across interfaces, even though the underlying governing equations are inherently and strongly coupled. Such explicit or semi-implicit treatments may limit numerical robustness and convergence efficiency, particularly for tightly coupled multiphysics systems. In this work, we present a block-coupled extension of the multiPhysicsFoam framework enabling volumetric coupling of multiphysics equations. The contribution focuses on a deep extension of the existing multiphysics architecture to support block-matrix assembly and solution within ESI OpenFOAM. The current implementation targets volumetrically coupled systems and provides a proof of concept for block-coupled pressure–velocity (p–U) formulations in incompressible Navier–Stokes flow, for which representative coupled benchmark cases are available in foam-extend. The proposed approach is validated against these benchmarks and compared with existing segregated, interface-coupled implementations. The results demonstrate improved numerical robustness, tighter convergence behavior, and a reduction in the number of outer iterations required for steady-state convergence. Furthermore, the impact of solver choices and under-relaxation strategies on computational efficiency is investigated. Finally, the applicability of the developed framework is demonstrated through industrially relevant cases, including injection molding simulations.