In the changing farming business, crop diseases and other challenges highlight the need for early detection and effective disease control to ensure global food sustainability. Deep Learning (DL) and Machine Learning (ML) are vital to plant leaf sickness identification, as this survey shows. Deep learning models like CNNs can automatically detect complex patterns in plant leaf photos, enabling reliable disease diagnosis. Mean- while, machine learning models like Support Vector Machines extract important elements from image data, providing valuable insights. Transfer learning with pre-trained models like VGG and ResNet and data augmentation improve model generalization. Deep learning (DL) models’ accuracy, precision, recall, and F1 score measure their ability to identify healthy and unhealthy plants. Deep learning (DL) and machine learning (ML) methods are used to diagnose plant leaf diseases in this survey. This study aims to synthesize knowledge, identify trends and tendencies, and suggest precision agriculture research directions. This survey seeks to advance sustainable crop management by understanding early and accurate disease detection.