<![CDATA[Early and accurate cancer detection remains a major challenge in modern healthcare. This study explores the emerging intersection between biomedical imaging and remote sensing technologies, focusing on the adaptation of microwave radar, thermal imaging, multispectral imaging, and hyperspectral imaging (HSI) for non-invasive cancer diagnosis. Originally developed for environmental and aerospace applications, these technologies are now being repurposed to detect subtle biochemical, morphological, and metabolic changes in human tissues that signal the presence of cancer. When integrated with artificial intelligence and machine learning, these imaging modalities enable real-time classification, high-throughput analysis, and enhanced surgical guidance. We highlight their advantages over traditional imaging methods and examine their application in detecting malignancies of the brain, breast, skin, oral cavity, and gastrointestinal tract. This review also discusses critical clinical and technical challenges, including the lack of standardized datasets, issues with device mobility, data complexity, and regulatory hurdles. Finally, we outline promising future directions such as edge-based data processing, explainable AI systems, multimodal imaging fusion, and ethical considerations for deployment in resource-constrained settings. This multidisciplinary approach has the potential to revolutionize cancer diagnostics, making them faster, safer, and more accessible, particularly for underserved populations.]]>
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