Our focus in cancer epidemiology lies in leveraging the potential of machine learning methodologies. Machine learning offers a promising avenue to unravel complex patterns and relationships within cancer data. The integration of advanced computational techniques with complex cancer data allows for a nuanced understanding of cancer dynamics, risk factors, and the interplay of diverse variables. Through machine learning algorithms, we aim to discern intricate patterns in large datasets, identifying novel biomarkers, elucidating predictive models, and optimizing interventions.