Cold-smoked salmon (CSS) is highly susceptible to spoilage due to its processing and storage requirements. This study leverages a multi-omics approach to unravel the complex interactions between microbiota, biochemical changes, and sensory characteristics during the storage of CSS produced in three distinct processing plants. By integrating high-throughput metabarcoding, volatile organic compound (VOC) profiling, biochemical assays, and sensory evaluations, plant-specific spoilage trajectories and molecular signatures that influence product quality were identified. Initial storage phases revealed a shared unspoiled profile across all samples marked by high levels of phenolic VOCs. However, as storage progressed, spoilage pathways diverged depending on the processing plant, driven by variations in microbiota composition and metabolic activity. Distinct bacterial communities, including genera such as Photobacterium, Aliivibrio, Carnobacterium, and Brochothrix, shaped the production of spoilage-related VOCs. Statistical analyses using the DIABLO framework uncovered strong correlations between bacterial taxa, volatile organic compounds (VOCs), and sensory attributes, emphasizing the distinct spoilage signatures associated with each processing plant. This study provides new insights into the spoilage mechanisms of cold-smoked salmon by integrating multi-omics data to identify plant-specific microbiota and their metabolic contributions. Beyond identifying distinct spoilage signatures, this study highlights the potential of multi-omics approaches to develop targeted interventions for maintaining product quality.