Wastewater-based epidemiology (WBE) has emerged as a valuable tool for monitoring the spread of human respiratory viruses, particularly in the context of the COVID-19 pandemic. By bypassing traditional clinical testing, WBE can serve as an early indicator for viral outbreaks, enabling communities to make informed public health decisions. While WBE has been primarily used for SARS-CoV-2, its potential extends to other human respiratory viruses, including influenza A and B, and respiratory syncytial virus. In this dissertation, we implemented a next-generation sequencing protocol to assess human respiratory virus RNA in both wastewater and nasopharyngeal swabs that PCR tested negative for SARS-CoV-2. Control mixtures containing synthetic human respiratory virus RNA were spiked into wastewater and nuclease-free water to evaluate any matrix effects on sequencing outcomes. Bioinformatics analyses used taxonomic classification and direct alignment methods to compare the accuracy of human respiratory virus identification in wastewater and clinical samples. Despite the potential of commercial next generation sequencing-based target-capture assays to detect viral genera, sequencing results from both wastewater and clinical samples demonstrated low depth and breadth of coverage, with discordant outputs from different bioinformatics pipelines. These findings highlight the need for rigorous benchmarking of laboratory and computational methods to ensure accurate human respiratory detection in wastewater and suggest that current sequencing approaches may fall short in providing the strain-specific information required for detailed public health surveillance.