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The primary objective of this research was to explore the effectiveness of Neural Radiance Fields (NeRF) in acquiring architectural forms and compare them with traditional photogrammetry results. The study began with a comprehensive literature review on AI in architecture and NeRF. Afterwards, a single case study applicable to both NeRF and photogrammetry was selected for comparison. The NeRF model showed the ability to accurately represent details and light effects, adapting reflections and transparencies to real-world conditions, as well as handling occlusions, and inferring three-dimensional information. In similar situations, Photogrammetry generated less coherent volumetrics or failed to interpret objects. Additionally, tests with a reduced number of images showed that the NeRF model maintained its characteristics, while photogrammetry suffered a decrease in quality and completeness. However, NeRF's performance was influenced by data collection quality. Insufficient data led to lower-quality volumetrics with imperfections, highlighting the importance of careful data collection, even with technologies like NeRF.