Introduction: Metabolic dysfunction-associated steatohepatitis (MASH) presents as a complex and multifactorial liver disorder characterized by inflammation, hepatocyte injury, and fibrosis, representing an urgent unmet medical need. Understanding the underlying genomic architecture of MASH is crucial for elucidating its pathogenesis and identifying therapeutic targets. In this study, we conducted a comprehensive meta-analysis of transcriptomics data derived from diverse murine experimental models to dissect the molecular mechanisms governing MASH progression. Objective: This study seeks to integrate RNA-seq datasets to comprehensively characterize the molecular landscape associated with MASH. Specifically, we aim to delineate dysregulated genes and pathways associated with pathogenesis with the overarching goal of advancing the understanding of the disease and finding potential therapeutic targets. Methods: A systematic approach was employed to curate publicly available RNA-seq datasets from GEO, ensuring stringent quality control and adherence to rigorous analytical protocols. Per-study Differential Expression Analyses using DESeq2 were conducted using an established pipeline. Subsequently, Fisher’s Combined Probability Test was applied to integrate statistical evidence across datasets, facilitating the identification of molecular signatures associated with MASH. Results: Our meta-analysis included data from 100 samples across 10 distinct murine MASH cohorts. We identified 11 genes whose expressions exhibited significant correlations with crucial pathological mechanisms including cytokine activation, lysozyme acidification, glycemic control, and insulin resistance. Importantly, these genetic signatures were intricately linked to the clinical manifestation of the disease. Conclusion: The meta-analysis revealed a comprehensive understanding of the genetic landscape underlying MASH, shedding light on key molecular pathways driving disease progression. The identification of 11 genes associated with pivotal pathological processes underscores their potential as therapeutic targets, offering promising avenues for the development of targeted interventions.