<![CDATA[This study uses a qualitative research method using a Systematic Literature Review (SLR) approach to comprehensively analyze optimal machine learning methodologies for medical image processing. The goal is to propose strategies to overcome existing barriers, thereby improving diagnostic accuracy and streamlining clinical workflows in healthcare through advanced machine learning applications. A literature search was conducted using three main data sources: Scopus, DOAJ, and Google Scholar, covering the period 2013 to 2024. Extensive application of machine learning (ML) techniques, especially deep learning models such as convolutional neural networks (CNN), has resulted in progress which is significant in medical image processing. These techniques have improved diagnostic accuracy and efficiency, overcome complex imaging challenges, and provided a powerful framework for disease detection, classification, and segmentation. This review aims to consolidate these findings and suggest future research directions to further integrate ML in medical imaging.]]>
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