Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.

In the past few years, big data has flattering more dominant in healthcare, due to three major reasons, such as the huge amount of data available, expanding healthcare costs, and a target on personalized care. Big data processing in healthcare refers to generating, collecting, analyzing, and holding clinical data that is too vast or complex to be inferred by classical means of data processing methods. Big data sources for healthcare include, the Internet of Things (IoT), Electronic Medical Record/Electronic Health Record (EMR/EHR) contains patient’s medical history, diagnoses, medications, treatment plans, allergies, laboratory and test results, genomic sequencing, Medical Imaging, Insurance Providers and other clinical data. This paper discusses different machine learning algorithms that were applied to various healthcare data. Also, the challenges of processing, handling big data, and their applications. The scope of the paper is to elaborate on the application of machine learning algorithms and the need for handling and utilizing big data from a different perspective.

Smart healthcare edge networks should be able to serve two purposes at once: to train federated machine learning models across a range of devices without violating patient privacy and to schedule other activities with latency constraints, like real‐time patient events. Such methods as FFL‐ANN attempt this by using fixed fuzzy rules, which do not work in the situation where the conditions of the network change in an unforeseen manner. In this paper, the framework FSI‐ANN is introduced to combine particle swarm optimization to quality‐aware model aggregation with ant colony optimization to adaptive real‐time task scheduling and ANN‐based predictions into a single framework. We experimented with FSI‐ANN on 200 edge devices. It achieved 0.825 precision compared with 0.82 with FedAvg and 0.80 with FFL‐ANN and reduced inference latency by 18%, 0.37–0.45 s. Throughput was maintained at 33 tasks/sec as compared with 27 of FedAvg. At burst load, the miss rate of the critical deadline was decreased by 90.2 percent and the energy consumed was decreased by 14.8% per round. The results suggest that adaptive learning using swarm is superior to the fixed rule‐based approaches and simple averaging in the distribution of resources at the sustainable healthcare advantage.