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Medicinal Plants Play A Vital Role In Traditional Healthcare Systems And Modern Pharmacology, Yet Their Accurate Identification In The Wild Remains A Challenging Task Due To Variations In Plant Morphology, Environmental Conditions, And Similarities Between Species. Traditional Identification Methods Rely On Expert Knowledge And Manual Inspection, Which Are Often Time-consuming, Subjective, And Impractical For Large-scale Applications. Recent Advancements In Computer Vision And Deep Learning Provide New Opportunities For Automated And Reliable Plant Recognition. This Study Presents A Medicinal Plant Identification System Based On Convolutional Neural Networks (CNNs), Designed To Classify And Recognize Plant Species Directly From Leaf And Plant Images Captured In Natural Environments. The Proposed System Leverages Image Preprocessing, Feature Extraction, And Classification Through CNN Models To Achieve High Accuracy Under Varying Lighting And Background Conditions. Such An Approach Not Only Facilitates Faster And More Accurate Plant Recognition But Also Contributes To The Preservation Of Traditional Knowledge, Supports Biodiversity Research, And Enhances Applications In Healthcare And Drug Discovery.