A prevalent form of cancer in female reproductive systems, cervical cancer arises often from a persistent infection with HPV with a multitude of symptoms varying in severity. An understanding of the pathological mechanisms with associated risk factors is critical to develop effective strategies at prevention, diagnosis, and treatment. The researchers developed an effective nomogram risk prediction learning model for adult cervical cancer by analyzing NHIS data from 2019 to 2023 (n=830). Utilization of machine learning algorithms and statistical analysis allowed for many variables to be screened to identify associative factors. They were able to isolate seven key categorical variables across age, poverty income ratio, and smoking status. The developed algorithm was highly accurate, allowing for future usage in clinical and public health sectors for risk assessment and intervention strategies.