Plant diseases and pests have a significant impact on both crop yield and quality, thereby posing a substantial threat to global food security. Traditional methods for identification and detection have limitations, resulting in substantial losses, particularly in countries such as India, where 35% of the annual crop yield is lost to plant diseases. This study explores publications spanning 2010 to 2023, showcasing the efficacy of advanced technologies in improving the accuracy and efficiency of plant disease detection. Therefore, this paper examines the advanced technologies already in use, specifically Machine learning technologies and deep learning technologies, to overcome the challenges of detecting plant diseases at early stage and large scale. Machine learning technologies, including clustering, decision making, classification algorithms and regression algorithms, as well as deep learning technologies such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer perceptron’s, Autoencoders, Generative Adversarial Networks (GANs), Graph Convolutional Networks (GCNs), attention mechanisms and Boltzmann Machines, emerge as promising solutions for early and large-scale plant disease detection. This research provides valuable insights for plant disease detection to researchers, practitioners, and industry professionals by offering specific information on plant diseases. In this paper, we discuss our approach to collecting and analyzing relevant literature, as well as the common methodologies used in many studies on plant disease detection. We also address limitations in existing reviews and suggest future research directions. Additionally, we provide an overview of publicly available datasets for leaf disease classification to aid researchers. Furthermore, we explore various machine learning and deep learning approaches and compare them. Moreover, we examine real-life applications related to leaf disease classification. Finally, we summarize our findings and suggest potential future research directions, concluding the paper with references for further reading