The implementation of virtual design and construction (VDC) and digital twin (DT) methodologies plays a crucial role in the digital transformation of the construction sector. Within the discipline of architecture, several BIM-driven systems are utilized to enhance design processes. These systems encompass computer-aided design data, parametric design processes data, generative design models, and the extensive data derived from building activities. These data sources serve as inputs for artificial intelligence models, enabling the integration of advanced computational techniques in architectural practices. Artificial intelligence (AI) models exhibit a high degree of complexity and find application across various academic disciplines. The utilization and theoretical frameworks surrounding the implementation of deep neural networks (DNNs) in the fields of construction and architecture have experienced a steady growth over time. The power of the relationship between building information modeling (BIM) systems and advanced artificial intelligence models holds considerable weight for users of BIM. This relationship allows the generation, analysis, and deduction of insights from substantial construction data. Generative adversarial networks (GANs), text-to-image techniques, diffusion networks, and large-scale language models possess promising prospects for utilization within the domain of the use of BIM design. This research examines the relationship between generative artificial intelligence (generative AI), deep neural nets, and the BIM system, including its users. The utilization of generative models in conjunction with the BIM system facilitates the simplification of the designer's tasks. The primary objective of this research is to investigate the amalgamation of building information modeling and state-of-art deep learning models. This study examines the correlation between generative artificial intelligence and the BIM system by conducting a case study. In this case study, a comprehensive textual depiction of the architectural component was formulated, and various alternatives were developed at varying levels of specificity. Furthermore, this paper examines the conceptual and practical use of generative AI components (e.g., diffusion models) in BIM systems via bibliometric analysis.