The proposed work aims to present a novel framework to classify the oral diseases through multimodal data consisting of images and symptoms. The outcome of the work helps towards early diagnosis of oral diseases. Oral diseases are neglected by most people in the initial stages due to lack of awareness, accessibility, and availability of dental care. Early diagnosis of oral disease is necessary, and early research focused on either the image of the affected area or just the textual description (symptom) to predict the illness; both are essential. The study employs multimodal approach and utilizes an image dataset comprising seven categories of affected areas, sourced from Kaggle. Additionally, a textual symptoms dataset was developed, consisting of 150 combinations for each illness. Confidence scores of both the model (image-classifier model and symptom-based illness prediction model) with the true label, a new dataset is generated and it is trained with logistic regression to get the final predicted class. Image classifier model achieved 81% of accuracy whereas symptom-based model 97%, resulting in final multimodal accuracy of surpassing both the model accuracies.