Banana diseases can have significant and wide-ranging impacts on agriculture, affecting both small-scale farmers and larger commercial plantations.Banana crops face significant challenges due to various diseases that can impact their yield and quality. This review delves into the current state of research, emphasizing the emerging role of machine learning in identifying and managing banana diseases. The literature surveyed highlights diverse approaches and methodologies employed to diagnose and mitigate these diseases. Furthermore, the abstract underscores the transformative potential of machine learning algorithms, demonstrating their efficacy in enhancing accuracy and speed in disease detection. The synthesis of existing knowledge in this area not only provides a comprehensive overview of the advancements made but also sets the stage for future research endeavors. Ultimately, this abstract serves as a valuable resource for researchers and practitionersinvolved in addressing the complexities of banana diseases to ensure sustainable banana cultivation and global food security.