Hair diseases are common health problems that affect the hair and scalp. These range from benign disorders such as dandruff to more serious ones such as Alopecia Areata, which causes hair loss. Because of these things, people are afraid to even face society. Hair illnesses are a significant public health problem, and early detection can help avoid hair loss and other consequences. Also, there are different doctors for these hair diseases and each doctor does not know about every disease. Therefore, patients do not have proper understanding about which doctor they should meet for this disease. Therefore, this is also a big problem that patients face. This study will help to identify coverage conditions, early diagnosis, guide the patient on healthy practices and even the treatment needed. Also symptoms of the patient, it is possible to predict which disease the patient is suffering from. Also this study helps to who are the right doctors for those hair diseases, where are those doctors located. Machine learning, Image processing, Internet of things and Natural language processing have been used for this. Here the disease can be identified by image processing and its accuracy is 98.52%. Convolution neural network, transfer learning is used for this. Also, deep learning model and Pytorch framework have been used to suggest the treatment required for the disease. Accuracy will be displayed on the notebook file. Also, deep learning model and Lang chain OpenAI, py pdf are used to predict the disease from the symptoms. Its accuracy is 95.52%. Also, EasyOCR, Lang chain has been used to analyze the disease by the patient's prescription and send necessary reminders. Lang chain is a generative algorithm. The proposed system seeks to provide a comprehensive solution for the categorization, diagnosis, and treatment of hair illnesses through the use of Internet of Things-enabled wearable sensors, machine learning algorithms, and natural language processing techniques.