The increasing industrialization of construction processes has intensified the demand for automated, scalable, and reliable inspection methods capable of supporting sustainable and resilient urban development. In particular, the validation of hydrosanitary installations in construction sites remains largely manual, leading to inefficiencies, increased costs, and limited traceability. This work presents an artificial intelligence–based approach for the automated detection, localization, and classification of components of industrialized hydrosanitary kits using region-based convolutional neural networks. The results indicate that deep learning–based object detection can effectively support automated visual validation in construction workflows, contributing to digital transformation, improved quality control, and increased operational efficiency. By enabling scalable and data-driven inspection processes, the proposed solution aligns with the United Nations Sustainable Development Goal 11, fostering more inclusive, resilient, and sustainable urban infrastructure.