<![CDATA[This study compares the roles of machine learning (ML) and deep learning (DL) in healthcare, focusing on their applications, challenges, and prospects. It addresses the increasing relevance of AI in public health systems and contributes a structured analysis of how ML and DL process different healthcare data types. A systematic literature review was conducted using sources from Google Scholar, Elsevier, Springer, IEEE, and MDPI, applying inclusion criteria based on relevance, publication quality, and recency (2018–2024). Article selection and synthesis using meta-analysis followed the PRISMA framework. The review identified four key application areas: (1) disease outbreak prediction, (2) disease forecasting, (3) disease diagnosis and detection, and (4) disease hotspot monitoring and mapping. ML techniques such as Random Forest and ensemble methods show high performance in handling structured data like patient records, whereas DL architectures like convolutional neural network (CNN) and long-short term memory (LSTM) are superior for unstructured data, including medical imaging and bio signals. Challenges common to both approaches include data quality issues, dataset bias, privacy concerns, and integration into existing healthcare infrastructures. Looking forward, promising directions include explainable AI (XAI), transfer learning, federated learning, and real-time data use from wearable and internet of things (IoT) devices. The study concludes that while ML and DL can significantly improve diagnosis, response to health threats, and resource allocation, maximizing their impact requires continuous cross-sector collaboration, transparency, and ethical governance.]]>
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