Agriculture plays a big role in many countries and is often the main source of income for a lot of people But plant diseases—caused by stuff like viruses, fungi, and bacteria—lead to serious losses in farming across the world Keeping an eye on crop quality and quantity means we need good disease management systems Usually, signs of disease show up on different parts of a plant, but the leaves are often hit the hardest. These days, researchers have started using tools like computer vision, deep learning, few-shot learning, and soft computing to spot diseases in leaves automatically. These technologies help farmers act fast and accurately, which can really help avoid major drops in crop yield and quality. Plus, these smart systems take away the need for manual work, like picking out features by hand or relying on slow, traditional ways of detection. On top of that, molecular techniques have also been introduced to deal with threats from pathogens. In this review, we look into how machine learning, deep learning, and few-shot learning are used in plant disease detection, talk about some diagnostic methods to prevent issues, and explore where this technology might be headed next.