Banana is one of the most important crops in the world and a strategic food crop in many developing countries. However, the cultivation of bananas is accompanied by various diseases and pests, which are a major threat to yield, quality, and economic returns. Timely and correct identification of disease is very essential in order to take necessary measures to avoid or reduce losses. In this paper, an advanced AI-based system for identifying diseases in bananas and suggesting treatment options is presented, while state-of-the-art deep learning techniques are used. Moreover, the system applies region-based segmentation and optimal thresholding methods to help the farmers with the right planting and spraying recommendations for disease control without the spread to other parts. This research integrates machine learning, image processing, and agricultural knowledge in the development of the model, thereby contributing to the improvement of banana production as well as the advancement of agricultural technology through the application of AI in sustainable farming.