Figures Abstract Background Ensuring medication safety requires accurate identification of antibiotic packaging, especially within pharmacy automation and dispensing systems. Advanced imaging and machine learning offer novel avenues for physical package recognition. Objective To investigate visual and textual features of antibiotic packages and evaluate their relationship with identification outcomes using unsupervised learning and efficiency-based analysis. Methods Thirty-six antibiotic formulations from Thailand (2016–2021) were analyzed using binary imaging, entropy metrics, packaging area ratio (PAR), and optical character recognition (OCR). K-means clustering was applied to segment package groups, and data envelopment analysis (DEA) was used to assess relative efficiency without assuming predefined functional relationships between inputs and outputs. Results Nine distinct image clusters were identified. Packages with mid-range entropy (7.1–7.5) and PAR (1.2–1.45) were associated with higher identification consistency. OCR text confidence influenced identification outcomes. DEA identified clusters with relatively efficient input–output configurations. Citation: Sukpornsawan P, Meepradist Y, Auamnoy T, Suksawatchon U, Chokchaitam S, Muongmee S (2026) Image-based identification and DEA-based optimization modeling of antibiotic packaging using unsupervised learning techniques. PLoS One 21(7): e0354277. https://doi.org/10.1371/journal.pone.0354277 Editor: Renier Mendoza, University of the Philippines Diliman, PHILIPPINES Received: July 5, 2025; Accepted: July 5, 2026; Published: July 27, 2026 Copyright: © 2026 Sukpornsawan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the manuscript and its Supporting Information files. Funding: This work was supported by the National Research Council of Thailand (Award No. 5.2/2562; recipient: SM; website: https://www.nrct.go.th). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. 1. Introduction Medication