Accurate and early diagnosis of potato leaf diseases is vital for boosting crop output and lowering agricultural losses, especially under uncontrolled field settings. This paper offers a deep learning-based multi-class classification system aiming at detecting various potato leaf diseases using leaf photos recorded in diverse, natural situations. Five famous convolutional neural network (CNN) architectures—MobileNetV2, InceptionV3, DenseNet169, VGG16, and VGG19—were tested utilizing a comprehensive pipeline involving picture preprocessing, augmentation, and dataset segmentation. Transfer learning was applied to utilize pre-trained weights, while fine-tuned classifiers were appended for domain-specific categorization. All models were trained using the Adam optimizer and early halting to ensure optimal generalization. DenseNet169 outscored others, obtaining the highest accuracy of 91.4%, along with greater precision, recall, and F1-score. VGG16 and VGG19 revealed consistent convergence with moderate accuracy, while InceptionV3 showed some overfitting tendencies. Evaluation criteria include confusion matrices and ROC-AUC curves proved the robustness of the models. Additionally, the investigation identified misclassification trends, particularly in visually comparable disease classifications. This work highlights the promise of deep learning in supporting real-time disease monitoring and scalable decision-making tools for smart agriculture. Future directions include the integration of explainable AI and deployment in edge computing settings for practical field applications.