With the approach of IoH (Internet of Health) era, conservative therapeutic or healthy ministrations are circumspectly migrating to the online and have been generating a significant volume of medical information relating with physicians, patients, prescribed drugs, and medical infrastructure and many. Scientific catastrophe detection and medical care services will benefit from the efficient fusion and analysis of this IoH data. Nevertheless, IoH data are habitually distributed athwart various departments and encompass incomplete user privacy. Consequently, it is frequently a thought-provoking task to efficiently incorporate or extract the intuitive IoH data, in the course of user privacy is not divulged. IoH data encompass a wide range of health-related information collected from various sources such as wearable devices, electronic health records, medical sensors, and patient monitoring systems. These data are valuable for understanding health trends, diagnosing medical conditions, predicting outbreaks, and enhancing healthcare services. For instance, in the context of disaster diagnosis, real-time health data from affected regions can provide critical insights into the impact of the disaster on public health, enabling swift and targeted response efforts. To overawe the previous section issues, here we recommend an innovative multi-source medical data incorporation and mining resolution for improved healthcare amenities, termed PDFM (Privacy—free Data Fusion and Mining). One of the significant hurdles in the healthcare domain, especially in the context of data-driven decision-making and research, is the concern over maintaining patient privacy and data security. Traditional data integration and analysis methods might inadvertently compromise patient confidentiality, which is a critical ethical and legal consideration in healthcare.