Timely cancer diagnosis remains a critical challenge in resource-limited settings due to infrastructural deficits. Previous reviews have broadly examined artificial intelligence (AI) in diagnostics, but less attention has been paid to offline systems. This review provides a consolidated synthesis focused specifically on fully offline, edge-deployed systems for cancer detection. Such systems execute autonomous, on-device inference without cloud dependency, a vital capability for deployment where connectivity is unreliable. A structured search of databases and gray literature from August to December 2025 identified 450 records. After screening, evidence from 31 studies meeting the inclusion criteria was synthesized; these encompassed cervical, breast, prostate, skin, and oral cancers. The quantitative evidence confirms that edge-deployed models can maintain clinical-grade diagnostic accuracy while enabling rapid point-of-care use. Key findings indicate that through optimization techniques such as INT8 quantization, diagnostic performance in tasks such as segmentation can be preserved with a reduction of less than 3% compared to standard models, as demonstrated in adapted U-Net architectures. Concurrently, these streamlined models achieved inference speeds above 155 frames per second on embedded hardware such as the NVIDIA Jetson, enabling real-time analysis. This review establishes that the combination of robust accuracy and high-speed offline operation is technically feasible. It thereby offers an evidence-based framework for utilizing edge AI to expand access to early cancer detection in underserved populations globally.