Potatoes play a significant role in Bangladesh’s agriculture, ranking third after rice and wheat, and contributing substantially to the nation’s economy. However, leaf diseases pose significant challenges to potato growth and quality despite Bangladesh being a major global potato producer. Detecting diseases in a timely manner is essential for enhancing productivity and advancing agricultural digitization. This study aims to utilize machine-learning algorithms on a limited dataset of potato leaf images for disease detection. The research focuses on classifying potato leaves into distinct disease groups using The Plant Village Dataset. A novel framework called “MultiNet” is introduced, which employs transfer learning to effectively categorize various potato leaf diseases. “MultiNet” facilitates rapid and accurate diagnostics through two multi-class classifications: (1) Early Blight, Late Blight, and Healthy (3 classes); and (2) Early Blight Normal, Early Blight Serious, Late Blight, Healthy, Insect infected, LeafRoll Virus infected, and Virus infected (7 classes). The system integrates three pre-trained models—ResNet50, DenseNet-201, and VGG16—to extract features from images, followed by concatenation to create a hybrid structure. The results demonstrate an impressive overall classification accuracy of 99.83% for three classes and 98% for seven classes. These findings underscore the potential impact and usefulness of the “MultiNet” framework in agriculture, suggesting promising applications within the sector.