The exponential growth in medical-related information presents all future hope of enhancing patient care in the healthcare industry, but at the same time, there is a presence of a wide range of data types. This paper aims to discuss the different types and forms of medical data “electronic health records (EHRs), medical imaging, genomic data, wearables, clinical trials, and social determinants of health” to review their relevance in the fusion of Artificial Intelligence (AI) in healthcare. Each data source is looked at as to how much of it can feed into personal and efficient healthcare before looking at potential problems associated with such data, like its quality and completeness, as well as its availability. The manuscript will then consider how different types of data can be integrated into a common platform to help AI systems have a broad and more detailed understanding of patients’ health status. From the analysis of the current approaches, it is possible to define recommendations on how to increase the efficiency of the data integrations and fusions to improve AI applications in Healthcare. Lastly, this paper reflects on future developments of an integrated medical data that will enhance the transformation of new AI models that can describe disease more conclusively and accurately, helping in the development support of precision medicine. The manuscript will also discuss the technology trends that will enable overcoming the existing challenges with data integration and how the unification of such disparate data sources might change the health decision-making process and patients’ treatment in the near future.