Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. However, current workflows are insufficient for proper risk assessment. Upon evaluating and optimizing techniques, we recommend best practices and introduce a well-structured workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, and is quantitative. The workflow was developed using a representative microbiome sample, which was created by aggregating 120 surface swabs collected from a medical intensive care unit. We recommend liquid-liquid extraction, propidium monoazide treatment coupled with internal standards and absolute abundance profiling (e.g., using qPCR), and a machine learning-based model for sequencing depth calculation. In addition, whole-cell filtration and cultivation may be valuable under particular circumstances. This workflow will contribute to more accurate and robust environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.