<p>Cancer, characterized by its complexity and heterogeneity, relies heavily on staging and grading to assess progression and prognosis. This review synthesizes insights from 222 research articles of computational models on the role of cancer staging and grading in diagnosis, treatment planning, and prognostic evaluation across breast, lung, liver, kidney, prostate, and skin cancers. Staging, often based on the tumour size, lymph node involvement, and metastatic spread, while grading evaluates histological aggressiveness through cellular differentiation and proliferation. Computational models, including machine learning and deep learning, enhance the precision and efficiency of these processes by integrating multi-omics data, imaging features, and clinical parameters. These models excel in early-stage detection for breast and prostate cancers and improve prognostic accuracy for lung and liver cancers through advanced feature extraction. For kidney and skin cancers, they address histological variability, enabling reliable grading via automated pattern recognition. This review highlights how these metrics are embedded in clinical guidelines, their impact on therapeutic outcomes, and the role of emerging molecular and imaging technologies in refining accuracy. Despite advancements, challenges such as inter-observer variability in grading and limitations in staging accuracy for certain types of cancer persist. Specific stage or grade analysis is a critical component of cancer staging or grading, enabling personalized treatment plans tailored to an individuals condition. These findings underscore the potential of computational models to standardize and personalize cancer staging and grading, paving the way for enhanced diagnostic precision and tailored therapeutic approaches in oncology.</p>