An increasing number of cancer imaging technologies are being developed using machine learning (ML) and artificial intelligence (AI). The ideal tool needs interdisciplinary collaboration to ensure that the right use case is taken up and to perform comprehensive development and testing before the tool arrives into healthcare systems. This paper will discuss the potential and challenges of AI and ML in cancer imaging, as well as recommendations for transforming algorithms into widely useful tools and creating the ecosystem needed to facilitate the field’s growth. The identification of metastatic cancer cells is essential for early cancer detection and staging. Nevertheless, during the onset of the disease, it is very tough to detect these cells from blood or biopsy samples. It has been noted that cancer cells, especially metastatic cancer cells, behave morphologically quite differently from healthy cells on aptamer-functionalized substrates. It has enabled us to pinpoint performance weaknesses in the model that can be methodically tracked and fixed. This comprehensive knowledge enables us to work around and beyond the model’s shortcomings, ultimately improving the standard of patient care and diagnosis. The model’s evaluation revealed an astounding accuracy rate of roughly 94.74%.