Bacterial biofilms are major contributors to persistent and chronic infections. Conventional methods used to quantify biofilms, such as the crystal violet assay, provide only semi-quantitative and non-specific estimates of their biomass. In this study, we highlight hyperspectral imaging (HSI) as a non-destructive approach for biofilm analysis. To address this, stainless steel coupons were developed to support robust biofilm formation. We propose HSI, coupled with machine learning tools, to detect and quantify Acinetobacter baumannii biofilms for potential real-time monitoring. This method has the potential to capture the biochemical as well as structural heterogeneity within biofilms and may serve as a valuable tool in biofilm research.