Plant disease detection and analysis are crucial for sustainable agricultural practices and food security. Deep learning approaches have emerged as a powerful solution for accurate, efficient, and scalable disease detection, surpassing traditional machine learning techniques. This comprehensive review explores the evolution of plant disease analysis models, focusing on deep learning architectures such as convolutional neural networks (CNNs), Generative Adversarial Networks (GANs), and Vision Transformers. Key components such as preprocessing methods—denoising, image enhancement, normalization, and segmentation—are reviewed, highlighting their role in enhancing model performance. The review also examines data diversity, feature extraction strategies, and model evaluation metrics, and identifies shortcomings such as class imbalance, scalability, and real-world deployment issues. Integration with real-time applications is considered crucial to advance precision agriculture. This review highlights the need for refining deep learning models, with multidisciplinary approaches, to ensure their effectiveness in diverse agricultural environments while addressing current limitations. Plant diseases have a significant impact on agricultural productivity, posing a threat to global food security and economic stability. Timely and accurate disease detection is essential to mitigate these impacts, but traditional diagnostic methods such as manual inspection and laboratory testing are often labor-intensive, time-consuming, and prone to human error. In recent years, deep learning (DL) in artificial intelligence has emerged as a transformative tool for plant disease analysis. This comprehensive review examines the development, applications, and challenges of deep learning models in plant disease detection and classification, and understands their potential and limitations.