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Wastewater-based surveillance (WBS) for SARS-COV2 is a valuable tool for monitoring dynamics of community disease burden through detection and quantification of viral RNA shed in feces. Wastewater data can exhibit significant variability, making it difficult to compare results over time and across sewersheds. To address this, studies employ a range of normalization techniques, yet the effectiveness of these methods remain unclear. This study systematically reviewed the application of normalization techniques in SARS-CoV2 WBS to assess their impact on data variability and uncertainty. We found that recovery control threshold values were the most widely applied technique overall, though their use declined between 2022 and 2024, while biomarker adjustments, particularly using the Pepper Mild Mottle Virus (PMMoV), became the predominant method in recent years. Bovine Coronavirus (BCoV) was the most used recovery control method. Clinical case correlation analysis was conducted in the majority of studies. Our analysis found that the most frequently used normalization techniques exhibited variability that is orders of magnitude greater than the variability they aim to control for, thereby increasing uncertainty and not reliably accounting for their targeted variability. Standardizing normalization approaches could improve the reliability of WBS trends, but the lack of clear confidence thresholds limits their interpretability. This study discusses how uncertainty can be communicated to decision-makers and examines how standardization and resource availability impact the effectiveness of WBS in different policy contexts. These results provide insights for future WBS normalization standardization and program implementation by informing program design and highlighting the effectiveness and uncertainty of normalization techniques.