Osteoporosis is a condition where one loses bone density which results in a weaker bone structure. Normally if we take a look at the bone structure of a person who is not affected by osteoporosis, we will observe that the structure looks like a honeycomb. A person affected by osteoporosis will have large holes in their bone structure which is due to loss of mass and bone density which reduces its strength simultaneously. Now one may not be aware of having the condition until a bone fracture happens. Therefore, in our paper we have tried to focus on various health factors which may increase the risk of one getting affected by osteoporosis. We have identified the most important features which play the most significant roles in predicting osteoporosis. We have also done a comparative study among machine learning algorithms and also between machine learning and deep learning algorithms in order to identify the model which is the most accurate in predicting the osteoporosis risk. We have received 89% accuracy in predicting the risk of osteoporosis along with high precision and recall.