Precise drug target discovery is pivotal to mitigating the escalating costs and high attrition rates that characterize pharmaceutical research and development. Given that traditional single-omics methods often fail to elucidate the systemic complexity of human diseases, deep learning (DL)-enabled multi-omics integration has emerged as a transformative frontier. This review systematically summarizes the advancements in DL-driven multi-omics integration for drug target discovery. First, the multi-omics data foundation and integration strategies are delineated, followed by an exploration of the DL architectures utilized for processing such data. Subsequently, the efficacy of DL-driven multi-omics integration is examined regarding the identification of novel disease drivers, prediction of synthetic lethality interactions, and prioritization of therapeutic targets. Finally, addressing persistent challenges related to data sparsity, model interpretability, and target druggability and validation hurdles, emerging opportunities driven by Generative AI, Large Multimodal Models (LMMs), Explainable AI (XAI), and multidimensional feasibility assessment frameworks are discussed in the context of advancing precision medicine.

While metabolomics captures the dynamic chemical landscape of biological systems, its inherent high dimensionality and complexity pose significant analytical hurdles. Interpretable Machine Learning (IML) is revolutionizing the field by moving beyond traditional feature selection to extract biologically meaningful insights alongside robust predictions. This review systematically examines IML’s application in metabolomic biomarker discovery. We highlight how interpretation frameworks decode the key metabolites driving model decisions, transforming opaque “black-box” algorithms into testable mechanistic hypotheses. By evaluating cutting-edge studies across various pathologies, we illustrate IML’s pivotal role in disease subtyping, early diagnosis, treatment prediction, and mitigating demographic disparities. Although challenges in data generalizability persist, IML remains an indispensable bridge between computational prediction and biological understanding, ultimately advancing precision medicine.