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Potato Is One Of The Most Widely Cultivated Food Crops Worldwide, Yet Its Productivity Is Severely Threatened By Leaf Diseases Such As Early Blight, Late Blight, And Leaf Spot. Early And Accurate Detection Of These Diseases Is Crucial For Minimizing Yield Loss And Supporting Sustainable Agricultural Practices. Traditional Disease Diagnosis Methods Rely Heavily On Manual Field Inspection, Which Is Time-consuming, Labor-intensive, And Prone To Human Error. Recent Advances In Deep Learning Have Demonstrated Significant Potential In Automating Plant Disease Detection By Extracting Discriminative Features Directly From Image Data. This Study Presents A Comparative Analysis Of Multiple Deep Learning Architectures, Including Convolutional Neural Networks (cnns), Vgg16, Resnet50, And Mobilenetv2, For Potato Leaf Disease Detection. The Models Were Trained And Evaluated On Publicly Available Datasets, And Their Performance Was Compared In Terms Of Accuracy, Precision, Recall, F1-score, And Computational Efficiency. Experimental Results Highlight That Transfer Learning–based Models Outperform Traditional Cnns By Achieving Higher Accuracy And Faster Convergence. Among The Evaluated Models, Resnet50 And Mobilenetv2 Demonstrated Superior Performance, Balancing Both Accuracy And Lightweight Computation, Making Them Suitable For Real-time Field Deployment. The Findings Of This Study Emphasize The Importance Of Model Selection In Building Robust Disease Detection Systems And Provide Valuable Insights For Developing Practical, Ai-driven Tools To Assist Farmers And Agronomists In Precision Agriculture.