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Leather Is One Of The Most Widely Used Natural Materials In Industries Such As Fashion, Furniture, And Automotive Manufacturing. However, The Presence Of Defects Such As Scratches, Cuts, Insect Bites, Wrinkles, And Stains Significantly Reduces Its Commercial Value And Usability. Traditional Defect Inspection Relies Heavily On Human Expertise, Which Is Time-consuming, Subjective, And Prone To Error, Especially When Applied To Large-scale Production. To Overcome These Limitations, Automated Leather Defect Detection And Classification Systems Have Gained Increasing Attention. This Study Presents A Deep Learning–based Framework For Accurate Detection, Classification, And Segmentation Of Leather Defects. Convolutional Neural Networks (CNNs) And Advanced Architectures Such As U-Net And YOLO Are Employed To Localize And Categorize Defects At Pixel And Region Levels. The Proposed System Ensures Both High Accuracy In Defect Classification And Precise Segmentation For Defect Localization, Enabling Manufacturers To Automate Quality Control Processes Effectively. Experimental Results Demonstrate The System’s Robustness In Handling Diverse Defect Types Under Varying Illumination And Texture Conditions.