Among the principal food cropsin the world, the banana, is seriously threatened by a number of illnesses that can lower productivity and quality. In order to suggest an appropriate fertilizer to lessen the effects of banana plant illnesses, we give a thorough analysis into these diseases in this paper. Our goal is to deliver a data-driven and intelligent solution to the intricate problems related to banana plant health by utilizing the capabilities of algorithms for machine learning and deep learning. The research starts with a comprehensive data gathering procedure that encompasses a wide range of information pertaining to diseases of banana plants, such as photographs, environmental factors, and past farming techniques. These datasets are utilized as the foundation for training deep learning models, which makes it possible to identify and categorize a variety of illnesses that impact banana plants. The study's conclusions have important ramificationsfor sustainable farming methods as they provide farmers with practical advice on how to best apply fertilizer and lessen the impact of illnesses that affect banana plants. Increased productivity and food security area result of improved agricultural management because the techniques of machine learning and deep learning that are employedto diagnose diseases and prescribe fertilizer, are so accurate and effective. Finally, this work offers a new and comprehensive method for treating illnesses of banana plants by fusing algorithms for machine learning and deep learningto deliver precise disease detection andcustomized fertilizer recommendations. The results might completely change how bananas are grown, paving the way for more robust and sustainable agricultural techniques in the future