This research paper presents an intelligent system for automated identification and classification of banana fruit diseases, along with an integrated grading system. The proposed system combines computer vision techniques, machine learning algorithms, and deep learning models to achieve accurate disease detection and grading. The system utilizes image processing techniques to extract relevant features from banana fruit images, which are then fed into a trained classification model. The classification model employs state-of-the-art algorithms to classify the banana fruit into different disease categories. Additionally, the intelligent grading system assesses the severity and quality of the infected fruit based on various parameters such as size, color, and texture. Experimental results demonstrate the effectiveness of the proposed system, showing high accuracy in disease identification and accurate grading of banana fruits. This automated system offers a time-efficient and cost-effective solution for disease management in banana plantations, facilitating early detection and effective decision-making for growers and agricultural stakeholders.