Background <p>Medical imaging has been essential and has provided clinicians with useful information about the human body to diagnose various health issues. Early diagnosis of diseases based on medical imaging can mitigate the risk of severe consequences and enhance long-term health outcomes. Nevertheless, the task of diagnosing diseases based on medical imaging can be challenging due to the exclusive ability of clinicians to interpret the outcomes of medical imaging, which is time-consuming and susceptible to human fallibility. The ensemble model has the potential to enhance the accuracy of diagnoses of diseases based on medical imaging by analyzing vast volumes of data and identifying trends that may not be immediately apparent to doctors. However, it takes a lot of memory and processing resources to train and maintain several ensemble models. These challenges highlight the necessity of effective and scalable ensemble models that can manage the intricacies of medical imaging assignments.</p> Methods <p>This study employed an SLR technique to explore the latest advancements and approaches. By conducting a thorough and systematic search of Scopus and Web of Science databases in accordance with the principles outlined in the PRISMA, employing keywords namely ensemble model and medical imaging.</p> Results <p>This study included a total of 75 papers that were published between 2019 and 2024. The categorization, methodologies, and use of medical imaging were key factors examined in the analysis of the 30 cited papers included in this study, with a focus on diagnosing diseases.</p> Conclusions <p>Researchers have observed the emergence of an ensemble model for disease diagnosis using medical imaging since it has demonstrated improved accuracy and may guide future studies by highlighting the limitations of the ensemble model.</p>