Real-time disease forecasting has become an extremely important branch of health care and is used to predict disease developments and time of diagnosis using new technologies. The efficiency of the computation in recognizing large volumes of data was made possible by the advancement of this particular method widely known as machine learning (ML) algorithms. This chapter offers comprehensive analysis of real time disease prediction systems powered by ML. It starts with the literature review where we express current findings, major contributions, and the lack of research in predictive healthcare. Thus, the state of the art of several subfield machine learning approaches, such as supervised, unsupervised, as well as deep ones, is examined in terms of real- time. It also introduces the necessary tools, datasets and data pre- processing for making use of these systems.' Details derived from the case study show enhanced feasibility of these algorithms in disease prediction with other features such as accuracy, precision, and response time demonstrated. © 2025 Elsevier B.V., All rights reserved.