Tuberculosis (TB) remains a global health crisis, exacerbated by rising drug resistance in Mycobacterium tuberculosis. While conventional therapies like BPaL/BPaLM regimens and shorter rifapentine-based treatments show promise 11–33, the need for novel anti-TB agents persists. Natural compounds, particularly from biodiverse regions like Indonesia 77, offer untapped potential, yet prior bibliometric analyses lack comprehensive integration of computational and multi-omics approaches to guide future research. This study maps the research landscape of natural compounds for TB treatment (2015–2025) through bibliometric analysis, identifying gaps and proposing AI-driven, multi-disciplinary strategies to accelerate drug discovery. PubMed-derived data (23 articles) were analyzed using VOSviewer to visualize co-authorship, keyword co-occurrence, and thematic clusters. Trends in authorship, geographic contributions, and research foci (e.g., molecular docking, drug resistance) were evaluated. China dominated research output (11/23 studies), with clusters emphasizing computational methods (e.g., virtual screening), bacterial enzymes, and animal models. Keyword analysis revealed a strong focus on drug resistance and synergism, yet limited exploration of AI, multi-omics, or ethnopharmacology. Notably, studies like Romulo et al. (2018) highlighted Indonesian plants’ anti-TB potential 77, but systematic integration with modern technologies remains underexplored. This study identifies a critical gap: the need to merge traditional natural compound research with AI-aided drug design, multi-omics (e.g., transcriptomics 1616), and nanodelivery systems. By proposing a framework that bridges computational predictions (e.g., molecular docking 1515) with experimental validation, this research advances a novel, scalable approach to combat drug-resistant TB, leveraging global biodiversity and cutting-edge technologies.