This chapter introduces how advances in multi-omics and machine learning are transforming the detection and management of food preservative residues. By weaving together insights from genomics, transcriptomics, proteomics, and metabolomics, scientists can now understand food matrices on a far deeper level, providing them with the tools to identify both familiar and emerging contaminants and to decipher how preservatives interact within complex foods. Modern approaches now extend far beyond the limits of conventional chemical testing, offering wider coverage and greater sensitivity. By applying machine learning, researchers can fully harness the potential of large and complex datasets, distinguishing genuine residue signals from background noise and uncovering patterns that would otherwise remain invisible. Recent advances have made it possible to achieve faster, more reliable, and even real-time checks on food safety, with some systems portable enough to be used directly on production lines. At the same time, important challenges remain, such as establishing strong standards, ensuring methodological consistency, and combining different types of omics data through artificial intelligence. Practical case studies demonstrate how this combination of tools is already improving continuous monitoring and creating predictive models that strengthen both food safety and product quality.