<p>Cancer ranks among the top causes of mortality worldwide, and its complex biological processes present significant hurdles for effective detection and management. Recently, meta-learning methods have been introduced as innovative approaches to refining cancer treatment and diagnosis. With the capacity to learn from sparse data and transfer knowledge across domains, these techniques are promising for enhancing diagnostic precision and therapeutic efficacy. Nevertheless, the application of meta-learning in cancer care raises several issues. Among these challenges, we can mention data limitations, methodological complexity, and the need for advanced expertise to ensure proper deployment. This article examines the application and issues of meta-learning methods in cancer research. This survey paper seeks to explore and evaluate the most recent advances in using meta-learning to diagnose and treat common cancers, including breast, skin, lung, prostate, and hybrid cancers, and to offer a holistic perspective on the opportunities and challenges of this approach. The findings of this study show that meta-learning can significantly enhance diagnostic precision and the efficiency of cancer treatment. The accuracy of diagnosis has been enhanced by up to 32%, and treatment outcomes have improved by up to 27%. Ultimately, by analyzing the prevailing challenges and suggesting solutions, this study will assist researchers and specialists in formulating novel strategies to enhance cancer treatment and diagnosis through a deeper understanding of meta-learning applications.</p>