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The Accurate Classification Of Pharmaceutical Drugs And Vitamins Plays A Vital Role In Ensuring Patient Safety, Preventing Medication Errors, And Supporting Effective Healthcare Delivery. Traditional Methods Of Drug Identification Often Rely On Manual Inspection Of Physical Features Such As Shape, Size, And Imprint, Which Are Time-consuming, Prone To Human Error, And Impractical For Large-scale Applications. To Overcome These Limitations, This Study Proposes A Deep Learning–based Automated Classification System That Leverages Convolutional Neural Networks (CNNs) Combined With Transfer Learning Techniques. Pretrained CNN Architectures Such As ResNet, VGG, And Inception Are Utilized To Extract Discriminative Visual Features From Drug And Vitamin Images, Significantly Reducing Training Complexity While Improving Classification Accuracy. The Proposed System Is Trained On A Curated Dataset Of Pharmaceutical Drug And Vitamin Images, With Preprocessing And Augmentation Techniques Applied To Enhance Generalization Across Varying Lighting And Imaging Conditions. Experimental Results Demonstrate That The Transfer Learning–based CNN Models Outperform Traditional Machine Learning Methods By Achieving High Accuracy, Precision, And Recall. The System Has Strong Potential For Real-world Applications In Healthcare And Pharmacy Sectors, Such As Mobile-based Drug Recognition Tools, Automated Pill Dispensers, And Intelligent Healthcare Management Systems. By Ensuring Reliable Classification And Reducing The Chances Of Medication Errors, The Proposed Approach Contributes To Safer, More Efficient, And Technology-driven Healthcare Practices.