Rice is one of the most significant primary crops and provides a substantial portion of the world’s food for billions of people worldwide. However, several leaf-related diseases pose significant risks to the rice crop’s yield and quality. The traditional approaches to disease identification and classification are frequently arduous and require specialized expertise. The domain of machine learning (ML), deep learning (DL), and the convergence of both of these techniques has emerged as a potential approach that can address this issue and enable immediate and accurate diagnosis of rice leaf disease. Additionally, this study offers a thorough analysis and contrast different approaches, focusing on addressing the initial research questions and identifying potential future possibilities. This study functions as a comprehensive resource for research groups engaged in the development of ML and DL applications specifically targeted at the categorization of rice leaf diseases. In addition to aiding various studies in creating feasible applications based on ML and DL techniques, this review effort will instruct the researchers on how these methods might facilitate the identification of rice leaf disease at an early stage. The insights derived from the study offer useful direction for the advancement of research related to rice leaf diseases.