Cancer remains one of the leading causes of morbidity and mortality worldwide, with its incidence demonstrating marked variability across different age groups. Understanding these patterns is vital for effective prevention, diagnosis, and resource allocation. This study provides a comprehensive analysis of cancer incidence rates stratified by age, employing advanced statistical methodologies to uncover meaningful trends and survival outcomes. Beginning with a broad epidemiological overview, we analyze data from national cancer registries, establishing foundational insights into age-specific cancer prevalence and associated demographic patterns. Recognizing the heterogeneity in cancer distribution, the study applies stratified sampling techniques to ensure that each age group is proportionally represented, enhancing the reliability of comparative incidence analysis. Following stratified data extraction, we implement survival analysis methods, including Kaplan-Meier estimation and Cox proportional hazards modeling, to assess temporal patterns in patient survival across age strata. The analysis highlights age-related disparities in cancer prognosis, with younger cohorts generally exhibiting higher survival probabilities due to earlier diagnoses and more aggressive treatment regimens, while older cohorts face lower survival rates influenced by comorbidities and late-stage detection. Additionally, hazard ratios quantify the risk differential between age groups, offering granular insights into survival determinants. This integrated approach not only underscores the importance of age-specific analysis in oncological research but also provides actionable findings for public health policymakers, clinicians, and cancer epidemiologists. By combining stratified sampling with robust survival modeling, this study delivers a precise and nuanced understanding of how age influences cancer incidence and patient outcomes, contributing to more targeted screening strategies and personalized care protocols.