The increasing complexity of medication usage, drug misidentification, and dispensing-related errors has created a significant demand for intelligent systems capable of automated pharmaceutical recognition. Traditional approaches for identifying drugs rely heavily on manual inspection, prescription interpretation, and human expertise, which may introduce delays and inaccuracies. This research presents a machine learning-based framework for automated drug detection and visual recognition in self-images, designed to identify pharmaceutical products from user-captured images through advanced visual feature extraction, classification, and recognition mechanisms. The proposed framework integrates image preprocessing, object detection, feature representation, and machine learning-based classification to enhance medication identification efficiency. The study conceptually investigates how artificial intelligence can support safer medication practices by reducing recognition errors and improving accessibility to drug information. Existing research on medication errors, prescription recognition, and pharmaceutical image analysis highlights the need for automated solutions that combine computer vision with intelligent decision-making capabilities (Gates et al., 2019; Nayak et al., 2023). Furthermore, human-centered AI trust modeling principles emphasize the importance of reliable and interpretable intelligent systems when applied to critical domains such as healthcare (Ramamurthy et al., 2026). The framework addresses current limitations in manual drug identification by providing a scalable approach for real-world applications, including personal medication management, pharmacy assistance, and healthcare support systems. The research contributes a structured machine learning architecture for improving drug recognition accuracy, reducing medication-related risks, and strengthening intelligent healthcare technologies.  

Machine Learning, Drug Detection, Image Recognition, Computer Vision

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