Cancer prognosis plays a critical role in guiding treatment decisions, predicting clinical outcomes, and improving patient survival. Traditional prognostic methods based on tumor staging and histopathological evaluation provide valuable information but often fail to capture the biological heterogeneity of cancer. Advances in molecular biology, genomics, proteomics, metabolomics, and bioinformatics have led to the identification of numerous biomarkers that can improve prognostic accuracy. Biomarker-based cancer prognosis models integrate molecular, genetic, proteomic, and clinical variables to predict disease progression, recurrence, treatment response, and overall survival. Recent developments in artificial intelligence (AI) and machine learning have further enhanced the ability to construct predictive models using large-scale biomarker datasets. This review explores the evolution, applications, methodologies, clinical significance, challenges, and future directions of biomarker-based cancer prognosis models. The findings suggest that integrating biomarkers into prognostic frameworks can significantly advance precision oncology by enabling personalized risk stratification and treatment planning.