Rice cultivation is an integral part of India’s changing landscape of agriculture. It offers an ALO-based Random Forest Classifier for accurate disease diagnosis and sorting in rice plants, including brown spot, bacterial leaf blight, and leaf smut. It begins with gathering the dataset from Kaggle and then continues the step of image pre-processing to upgrade its quality. Feature extraction extracts statistical features based on GLCM, whereas the segmentation applies the Otsu threshold approach. The optimized RFC is done using ALO for better precision. High accuracy rates and lower computation times are ensured and validated compared with alternatives, affirming that such a framework in rice plant disease detection and classification works well.