Effective monitoring of architectural surfaces is essential for timely maintenance planning. Accurate and systematic assessments enable early intervention, preventing severe deterioration and costly repairs. Traditional inspections rely on visual examinations by skilled technicians, making them subjective, time-consuming and limited to what is visible. In such perspective, Spectral Imaging (SI) is emerging in the construction sector as a valuable advanced diagnostic tool. By integrating spectral and spatial data, SI provides a reliable, non-destructive method for detecting material alterations with high precision. SI delivers spatially referenced data, enables automated analysis of decay phenomena, and, by leveraging wavelengths beyond the visible spectrum, identifies changes before they become apparent to the human eye. This study presents a real-world case demonstrating the workflow for detecting and classifying alterations and decay on architectural surfaces using SI. Hyperspectral images—meaning comprising hundreds of spectral bands—of a concrete surface on a university building in Ancona, Italy, were acquired using a sensor covering the 350–1000 nm spectral range. After optical and spectral calibration, data were processed using a Machine Learning (ML) algorithm, producing a segmented classification of surface alterations. The results were compared to a manual survey by a technician. The SI-based assessment proved to be accurate, comprehensive, and significantly more time-efficient than the traditional approach. This study underscores the potential of SI as a powerful tool for automated architectural diagnostics, facilitating proactive maintenance strategies and optimizing conservation efforts in the built environment.