Plant diseases pose a significant threat to agricultural productivity, leading to reduced crop yields and economic losses. Traditional disease detection methods rely on manual inspection, which can be time-consuming, inconsistent, and impractical for large-scale farming. This study explores the use of artificial intelligence (AI) in automating plant disease classification by leveraging deep learning techniques. A dataset of healthy and diseased leaf images was sourced from the Kaggle PlantVillage dataset. A pre-trained VGG16 Convolutional Neural Network (CNN) was fine-tuned using transfer learning, achieving a mean validation accuracy of 95% (peak 100% in best iteration). Data augmentation techniques, including image rotation, flipping, and zooming, were applied to enhance model robustness, while hyperparameter tuning optimized training performance. The model’s best iteration demonstrated 0.0009 loss with full class separation, validating its reliability. The findings highlight AI’s potential to support precision agriculture by enabling early and reliable disease detection, reducing dependence on human expertise. Future work will focus on real-time deployment in mobile applications, improving model robustness for diverse environmental conditions, and integrating AI with IoT-based smart farming systems to optimize crop health monitoring.