This research paper centers on the pivotal role of sugarcane cultivation in India's economic development. Sugarcane, a versatile crop, serves diverse purposes encompassing brown sugar production, animal feed, white sugar, bio-electricity, and bio-ethanol. Addressing the needs of an expanding global populace necessitates augmenting sugarcane yields. However, productivity confronts significant threats from pests and diseases, resulting in substantial economic repercussions. Timely detection of these issues is imperative for proficient pest management and productivity augmentation. The study underscores the imperative for automated detection and early diagnosis of sugarcane diseases, elucidating the limitations of manual visual inspection. To surmount these challenges, the application of image processing algorithms is pivotal in promptly extracting features from sugarcane leaves and discerning ailments in their nascent stages. The research offers an exhaustive overview of diverse image processing techniques and deep learning methodologies for effective sugarcane disease detection and expeditious assessment. Furthermore, it scrutinizes the intrinsic challenges in computational approaches for appraising sugarcane infections and delineates potential future trajectories. Overall, this inquiry accentuates the critical import of precise disease detection in sugarcane crops, accenting the considerable economic ramifications of diseases on agricultural output. Harnessing intelligent computational tools empowers farmers to proactively combat sugarcane diseases, culminating in ameliorated crop quality and augmented yield. This paper furnishes an invaluable resource for researchers, agronomists, and technologists in pursuit of cutting-edge techniques propelling advancements in sugarcane disease detection, thereby laying the groundwork for the formulation of more efficacious and streamlined disease management strategies in the sugarcane industry.