In this study, a machine learning (ML) based method is proposed for the early detection of plant leaf diseases. Tea leaf disease detection plays a crucial role in enhancing agricultural productivity and ensuring the health of tea plantations. In this study, a comprehensive methodology for tea leaf disease detection utilizing image processing (IP) and machine learning (ML) techniques is presented. Our approach involves the extraction of diverse features from tea leaf images, encompassing color, texture, shape, and size attributes to capture a wide range of characteristics indicative of disease presence. These features are then utilized to train and evaluate a classification model capable of accurately distinguishing between healthy and diseased leaves. Experimental findings underscore the efficacy of our proposed approach in accurately identifying tea leaf diseases, thus facilitating the advancement of plant disease management strategies within the tea cultivation domain, enhancing agricultural productivity, and ensuring the health of tea plantations.