Systems bioinformatics integrates computing, statistics, and systems biology to model complex molecular interactions using network-based methods. This field advances precision medicine by combining multi-omics data to discover disease mechanisms and support. A key application is network pharmacology, which uses a multicomponent-multitarget perspective to investigate compound-target-pathway interactions, which is particularly useful for natural product-based drug discovery. Recent advances in artificial intelligence (AI) have improved this integration by enabling network target identification through relationship mining, target association, and navigation strategies, moving away from descriptive analyses toward predictive and mechanistic insights. This paradigm represents a shift away from the conventional single-compound-single-target model, which is frequently ineffective for multifactorial diseases, and toward synergistic, systems-level strategies. To fully realize this potential, we propose a structured framework that includes in silico prediction, pharmacological evaluation, extract standardization, preclinical and clinical testing, and metabolomic analysis. Building on this, we develop a workflow for precision medicine using natural products that includes disease network analysis, target protein selection, genotype–phenotype association, machine learning-based enrichment analysis, and virtual screening. This workflow is used to examine the metabolite profile of Murraya paniculata and its mechanism of action on adipose tissue. Finally, we introduce the integrative platform for research in the Indonesian medicine ecosystem (I-PRIME), a digital platform that bridges Indonesia’s biodiversity with biomedical informatics to accelerate translation into evidence-based clinical therapies. This section focuses on I-PRIME as a technical and conceptual framework for developing natural product-based precision medicine in Indonesia.