Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition associated with high short- and long-term morbidity and mortality. One major limitation in the management of ARDS is its biological and clinical heterogeneity, which may explain the lack of consistent benefit observed for most therapeutic interventions in unselected patient populations. Recent studies have suggested the existence of distinct ARDS subphenotypes, potentially characterized by unique inflammatory or metabolic signatures, which may respond differently to treatment. This supports the need for standardized tools to identify these subgroups and develop personalized therapeutic strategies. Methods: This manuscript describes a standardized protocol for metabolomic and cytokine profiling of biological samples from ARDS patients. Specifically, we outline detailed procedures for the collection and processing of serum and bronchoalveolar lavage fluid, and for the subsequent multi-omic analysis. Metabolomic profiling is performed using gas chromatography–mass spectrometry (GC–MS), following a validated sample preparation and derivatization workflow, allowing both targeted and untargeted metabolic analysis. Cytokine profiling is conducted using a Luminex® multiplex immunoassay platform, enabling the simultaneous quantification of multiple inflammatory mediators from low-volume samples. The manuscript also provides recommendations on sample quality control, data integration with clinical and imaging parameters, and multivariate statistical approaches for data interpretation. Conclusions: The described approach enables high-throughput, standardized, and reproducible molecular profiling of ARDS patients across different clinical studies. It is intended to support the identification of ARDS subphenotypes based on inflammatory and metabolic signatures, and to foster the integration of biological data into personalized clinical decision-making. This may serve as a methodological foundation for future prospective investigations aimed at improving outcome prediction and tailoring therapy in patients with ARDS.