The article introduces Multidimensional Bayesian Networks (MBNs), an advanced extension of traditional Bayesian Networks (BNs) and Object-Oriented Bayesian Networks (OOBNs). While OOBNs are useful for modelling complex systems by modularizing subsystems into objects and classes, they struggle with heterogeneous components and inference computation in multi-dimensional spaces. MBNs address these issues by assigning multiple indices to variables, enabling the modelling of large systems across various dimensions, such as space and time. The article provides an overview of OOBNs, highlighting their advantages, such as modularity, reusability, and scalability. However, it also discusses limitations, including computational complexity and the challenges of hierarchical modelling and inference, particularly for large-scale systems. The proposed MBN model overcomes these challenges by expanding dimensionality, with key innovations like "virtual nodes" and a structured inference process that allows local computations within objects before propagating information across the system. The new algorithms and inference methods introduced allow for more efficient handling of complex relationships in multidimensional systems, making MBNs a powerful tool for decision-making in fields requiring large-scale, dynamic modelling.