Multifactorial diseases arise from complex interactions among various biological systems. These interactions represent intricate molecular mechanisms leading to pathological conditions. Studying these mechanisms requires dedicated analytical methods capable of integrating diverse omics data to model disease progression. Traditional analytical methods focus on the integration of individual genetic, transcriptomic, or metabolic datasets which are insufficient for capturing this complexity. Integration of one layer of omics data may lead to significant gaps in our understanding of complex diseases. To address this limitation, we present Metabopathia, an extension of HiPathia, as a computational tool that integrates multi-omics data—including transcriptomics and metabolomics—into mechanistic models of signaling and metabolic pathways. The method leverages curated biological knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG), which provides the pathway structures used to model these processes. By doing so, Metabopathia enables accurate measurement of changes in the activity of cellular signaling cascades. This not only supports high-throughput estimation of functional cellular profiles, but also allows the simulation of how genetic mutations that reduce or eliminate gene function, along with metabolic disruptions, affect cellular processes. The application of Metabopathia to complex diseases such as cancer has demonstrated its efficacy in identifying critical sub-pathway alterations that drive disease progression. By incorporating metabolomic data, Metabopathia improves the mechanistic understanding of multifactorial diseases and informs potential therapeutic strategies tailored to disease-specific molecular contexts.