Cancer stage at diagnosis is a critical determinant of survival outcomes and a key metric for population-based cancer surveillance. Despite the existence of several cancer staging classifications implemented in registries worldwide, their relative utility remains poorly understood. This review provides a comprehensive and comparative evaluation of the principles, data requirements and practical utility of the traditional tumor-node-metastasis (TNM), Surveillance, Epidemiology and End Result Summary, Condensed TNM, Essential TNM, registry-derived and extent-of-disease staging systems. It also introduces a conceptual framework for evaluating these systems, in order to aid registries in selecting context-appropriate staging methods. Our appraisal, focusing primarily on aspects pertaining to data collection and consolidation, recognises that while the traditional TNM system offers the highest clinical and prognostic value, its complexity leads to poor completeness in population-based registries, particularly in low- and middle-income countries. Simplified alternatives can achieve higher completion rates but offer limited clinical utility. A balanced approach jointly incorporating clinical value and practical feasibility is essential, highlighting the need for hybrid solutions to support cancer registration. Electronic aids such as staging applications and natural language processing or AI-driven tools can streamline staging by automating data extraction, minimising errors and inferring missing components. Future efforts must prioritise accessible, multilingual platforms to standardise surveillance and improve accuracy in resource-limited settings.