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Abstract Machine learning (ML) and nanotechnology interfacing are exploring opportunities for cancer treatment strategies. To improve cancer therapy, this article investigates the synergistic combination of Graphene Oxide (GO)‐based devices with ML techniques. The production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the investigation. GO is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal chemicals. Then, complicated biological data are analyzed using ML algorithms, which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular characteristics. The study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using ML. Predictive modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery systems. Furthermore, tracking treatment outcomes in real time is made possible by ML algorithms, which permit adaptive modifications to therapy regimens. By optimizing medication doses and delivery settings, the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.
Das et al. (Sat,) studied this question.