One of the most common diseases which acts as normal but poses a highest crucial role in dermatology which requires fast diagnosis and an efficient therapy. In the traditional times, the dermatologist’s approach is based on the subjective diagnosis only which only include the visual inspections which can be more time consuming and prone to errors. As with the enhancement in the technologies, and the world’s cycle goes more with Artificial intelligence and Machine Learning approaches. So, in this paper we are using the Machine Learning approaches for the automation of detection procedure using different ML algorithms and Deep learning approaches to get the desired results which provide accurate and trustworthy results in an early stages. By using these methods, the machines aims’ to provide the better or accurate diagnosis, accurate results about the hair disease detection which is better than visual inspection and hence results in less time consumption for the dermatologists. The proposed model includes the multi stage techniques include data preprocessing, getting the best feature extraction using different ML techniques and then classify the data to detect different hair disorders. Deep learning methods such as CNN (Convolutional Neural Networks) is also used for detection based on the visuals datasets to achieve the accuracy. The system should be trained and tested using wide range of images. By assisting dermatologists in correctly diagnosing hair issues, this approach hopes to enhance patient care and treatment results.