In this paper, a machine learning model is used to develop a model that is used for tuberculosis prediction. Tuberculosis is known to be one of the top reasons for death from an infectious agent that affects the lungs and continues to threaten the human population on a wider basis. According to WHO, tuberculosis is a serious threat to the human population after HIV/AIDS. It is estimated by the World Health Organization (WHO) that 1/3rd of the global population is infected with TB and that seven to eight million new cases of TB occur each year across the globe Because the disease is difficult to differentiate between the common cold, it takes a long time to decide the patient is affected by the disease. So we use the detection of tuberculosis by utilizing the K-NN algorithm method for classification and HOG as feature extraction. K-NN abbreviated as K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on the Supervised Learning technique.