Osteoporosis (OP) also known as porous bone, is a serious illness wherein individual's bones weaken, increasing the likelihood of fractures. OP is caused by micro-architectural degradation of bone tissues, that raises the probability of bone fragility and can result in bone fractures even when no force is placed on it. The estimation of bone mineral density (BMD) is a prevalent method for detecting OP. For women who have reached menopause, prompt and precise forecast and preventative measures of OP is absolutely essential. BMD can be measured using imaging methods like Computed Tomography (CT) and Dual Energy X-ray Absorptiometry (DEXA/DXA). Blockchain (BC) is a revolutionary technique that is utilized in the health sector to store and sharing patient information between clinics, testing centres, dispensaries, and practitioners. The application of blockchain could perhaps reliably detect drastic and even serious errors. As a result, it has the potential to enhance the privacy, and disclosure of clinical data exchange in the health care sector. This system helps health organizations in bringing awareness and improving the evaluation of health records. By integrating the blockchain technology with machine learning algorithms, the various bone disorders such as Osteopenia, Osteoporosis and Osteoarthritis can be identified in earlier stage, that delivers a report regarding the prediction of fracture risk. The developed system can assist the physicians and radiologists for more rapid and better diagnosis of the affected ones. In this paper, we propose a fully automated mechanism for suspicious osteoporosis patients that uses machine learning techniques to improve prognosis and preciseness via different processes. Here we developed an automated system that integrates Principal component analysis (PCA) with weighted k-nearest neighbours algorithm (wkNN) to identify, predict and classify the BMD scores as Normal, Osteopenia and Osteoporosis efficaciously. The classified results are validated with the DEXA scan results and by the clinicians to demonstrate the efficacy of the machine learning techniques employed.The laboratories use BC to safely and anonymously share the findings with the patient and doctors.