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However, despite their pharmacological richness, systematic translation of plant\u2010derived metabolites into clinically approved drugs remains constrained by persistent bottlenecks, including extract complexity, dereplication redundancy, structural elucidation challenges, taxonomic ambiguity, and multi\u2010component pharmacology. This review presents a bottleneck\u2010driven and systems\u2010oriented framework for integrating artificial intelligence (AI) into medicinal plant\u2010based drug discovery (MPDD) to address some of these bottlenecks. Rather than reiterating broadly documented AI tools used in synthetic drug development, the manuscript critically examines phytomedicine\u2010specific challenges and maps AI applications across key stages of the pipeline: medicinal plant identification, extraction optimization, plant metabolite identification and dereplication, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, virtual screening, network pharmacology, repurposing, and generative de novo design of pseudo\u2010natural products. Emphasis is placed on the foundational requirement of making phytochemical datasets truly AI\u2010ready through metadata harmonization, dataset balancing, novelty\u2010aware modeling, and structured data engineering. Emerging approaches such as transformer\u2010based foundational models, graph neural networks (GNNs), generative AI (GAI) architectures, and multi\u2010omics\u2010integrated network pharmacology are discussed within a pragmatic translational context. Importantly, this review maintains a balanced perspective, acknowledging that fully AI\u2010driven clinically approved botanical drugs\/synthetic drugs have yet to emerge and identifies challenges limiting the progress. By integrating computational methods with infrastructural reform, this work outlines a progressive roadmap to transition AI in medicinal plant research from exploratory studies toward standardized, reproducible, and biologically grounded next\u2010generation drug discovery.<\/jats:p>","DOI":"10.1111\/cbdd.70372","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T11:40:04Z","timestamp":1785498004000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges"],"prefix":"10.1111","volume":"108","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3228-5437","authenticated-orcid":false,"given":"Amit","family":"Gangwal","sequence":"first","affiliation":[{"name":"Department of Pharmacognosy SVKM NMIMS Global University School of Pharmacy and Technology Management  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