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Data in the healthcare industry and machine learning techniques is useful to analyse a huge amount of data to identify the hidden patterns in the disease, to give personalised treatment for the patient and also used to predict the disease. The main intent of this paper is to plan for a systematic review on medical storage systems, noise removal techniques for medical images and multi-disease prediction using artificial intelligence. This paper conducts a algorithmic contributions used for medical storage systems, noise removal in medical images and multi-disease prediction that are analysed. Moreover, the performance metrics measured in various contributions adopted tools, and different datasets utilised for solving those problems are surveyed. Finally, a strong research gaps and challenges that will pave the way for creating a room for future research perspective are explored. The suitable research gap is focused with the existing limitations in traditional medical storage systems, noise removal methods and multi-disease prediction models that could motivate the researchers to focus on developing new models. Thus, with a strong integration of biomedical and healthcare data, modern healthcare organisations can possibly revolutionise the medical therapies and personalised medicine

Research challenges and future directions towards medical data processing