N6-methyladenosine (m⁶A), the most abundant post-transcriptional modification in eukaryotic mRNA, plays pivotal roles in diverse biological processes. Dysregulation of m⁶A levels has been implicated in numerous human diseases, particularly cancer. Although several computational tools exist for predicting putative m⁶A sites, none have specifically addressed the identification of cancer-associated (or pro-cancer) m⁶A residues at single-base resolution. To address this gap, we developed m6A-CAPred, a computational framework for accurate prediction of cancer-associated m⁶A sites at base resolution. Our model was trained on a comprehensive dataset comprising experimentally validated m⁶A sites from 25 cancer cell lines and 23 normal tissue samples. Initial analysis revealed that sequence information alone only provided limited predictive performance. However, by incorporating genomic context features, m6A-CAPred achieved significantly improved accuracy (average AUROC = 0.885 on independent test sets), successfully capturing distinct characteristics between cancer-associated and normal m⁶A sites. We then applied m6A-CAPred for transcriptome-wide prediction to screen for potential cancer-associated m⁶A sites. The somatic variants derived from 33 types of TCGA cancer projects were extracted for additional validation, and the results showed that SNP density clearly differentiated the predicted pro-cancer and normal m⁶A sites, further confirming the model's biological relevance. Additionally, the cancer-associated m⁶A sites showed significant enrichment in functional important biological processes and cancer-related pathways. We hope that m6A-CAPred will serve as a valuable resource for epitranscriptome research, with potential applications in cancer biomarker discovery and therapeutic target identification.