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Nail Diseases, Including Fungal Infections, Psoriasis, And Onycholysis, Are Common Health Concerns That Affect Both Aesthetics And Overall Well-being. Traditional Diagnostic Methods Rely Heavily On Visual Inspection And Clinical Expertise, Which Are Often Subjective, Time-consuming, And Prone To Human Error. With The Rapid Advancements In Artificial Intelligence, Deep Learning Has Emerged As A Powerful Tool For Automated Medical Image Analysis. This Study Proposes SMART Diagnosis, A Deep Learning–based System Designed To Accurately Identify And Classify Various Nail Diseases From Digital Images. By Leveraging Convolutional Neural Networks (CNNs) And Transfer Learning Techniques, The System Can Extract Discriminative Features From Nail Images, Enabling Precise Disease Classification. The Proposed Model Aims To Assist Dermatologists In Early And Reliable Diagnosis, Reduce Diagnostic Errors, And Provide Scalable Solutions For Telemedicine Applications. Experimental Results Demonstrate High Accuracy And Robustness, Highlighting The Potential Of SMART Diagnosis To Transform Nail Disease Identification Into A Faster, More Accurate, And Accessible Process, Ultimately Improving Patient Care And Health Outcomes.