Plant leaf disease detection is vital in ensuring agricultural manufacturing and food security. Conventional methods for identifying and diagnosing plant diseases are often time-consuming, and there is a possibility of human error. In recent years, deep learning (DL) and computer vision (CV) techniques have revolutionized the field of agriculture, offering automated, precise, and scalable solutions for disease detection. The comprehensive review explores these technologies’ valuable impact on plant leaf disease detection. It examines the significant advancements made in the application of convolutional neural networks (CNNs), transfer learning, and other DL architectures, which have markedly improved the results and speed of disease identification. The review also addresses the challenges encountered in real-world implementations. Furthermore, the paper discusses the necessary trends and future directions, including the integration of Internet of Things (IoT) devices and edge computing, which promise to enhance the effectiveness of disease detection systems further providing a thorough analysis of the current state and future potential of DL and CV in agriculture.