Betel vine, known for its economic and cultural significance, is prone to various diseases that can severely impact its yield and quality. Traditional disease detection and diagnosis methods in betel vine crops often rely on expert visual inspection, which can be time-consuming and subjective. The study suggests machine learning based automated method for examining diseases in betel vines. The proposed work focuses on detecting and quantifying the impact of a disease on betel vine leaves using a Machine Learning algorithm. By leveraging a vision-based strategy, the proposed method effectively detects and analyzes external signs of illness. Machine learning techniques are used to locate the disease-affected portions of the leaves. Subsequently, the affected area is measured and extracted based on the collected data on plant features. Integrating modern advancements in machine learning has significantly enhanced the performance and accuracy of disease detection in betel vine leaves. This research aims to develop a cost-effective and efficient approach for studying diseases in betel vine leaves, catering to the needs of farmers and agricultural researchers. The results indicate a classification accuracy of 98.73 per cent for disease categorization.