Environmental Monitoring Programs (EMPs) are an essential component of food safety systems and involve routine sampling of the food processing environment to verify preventive controls and take corrective actions if needed. Environmental data collected during EMPs require statistical methods to summarize microbial levels, characterize trends, and support data-driven decisions. However, the statistical approaches used to analyze EMP data vary widely, and no comprehensive assessment describes how these tools are applied in practice. Therefore, we conducted a systematic review to investigate the statistical tools used to analyze EMP data, identify factors influencing their selection, and assess their suitability for different objectives. Literature published from 2000 to 2025 was identified through a SCOPUS keyword search and screened in Covidence following PRISMA 2020 guidelines (title and abstract screening, full-text review, and extraction). Of the 1,864 records, 55 were included for full-text review, and 50 met the eligibility criteria for qualitative analysis. Descriptive statistical tools (n = 49) were most used to summarize EMP results, including microbial counts and prevalence, in frequency distribution tables. Inferential tools (n = 31) were used to compare contamination among locations, samples, or time periods and to assess associations. Visualization methods (n = 13) were used to graphically illustrate spatiotemporal contamination trends. Advanced statistical methods, including regression models (n = 10) and multivariate analyses (n = 4), were used to link contamination outcomes to environmental factors and to predict high-risk scenarios. Overall, our findings show that EMP data are predominantly analyzed using basic descriptive methods, highlighting the need for advanced statistical tools. However, tool selection should be guided by the EMP objectives, study design, and the type of data collected. @ https://doi.org/10.1016/j.jfp.2026.100766