Detecting rare and complex cancers, particularly skin cancer, remains a significant clinical challenge due to tumour heterogeneity, insufficient clinical evidence and limitations of conventional diagnostic methods. Radiotherapy, a key treatment modality for cancer, faces challenges in precision and personalization due to the variability in tumour biology and the lack of reliable prognostic biomarkers. Recent advancements in multi-omics approaches, which integrate genomic, transcriptomic, proteomic and metabolomic data, offer a promising solution for improving cancer detection and treatment. We explore the integration of artificial intelligence (AI) with multi-omics data to enhance the efficacy of radiotherapy for rare and complex cancers. AI-driven techniques, particularly machine learning and deep learning, facilitate automated tumour segmentation, dose prediction and treatment response modelling. These methods enable the analysis of complex multi-omics data, providing a deeper understanding of tumour characteristics and improving treatment precision. Additionally, AI-powered image preprocessing techniques, such as contrast stretching and noise reduction, enhance the quality of automated skin cancer detection systems. The integration of multi-omics data helps in identifying novel biomarkers for early cancer detection and personalized treatment strategies. Despite the promising potential of AI and multi-omics, several challenges remain, including data heterogeneity, the need for large annotated datasets and the lack of model interpretability. Overcoming these challenges requires advancements in explainable AI and further research into real-time adaptive radiotherapy systems. The future of AI-driven radiotherapy, supported by multi-omics data, holds the potential to revolutionize cancer treatment, paving the way for precision oncology that tailors treatments based on the unique biological characteristics of each patient.