Cervical cancer continues to be a predominant cause of illness and death among women globally, highlighting the essential requirement for prompt and precise diagnosis. This study implements Kolmogorov–Arnold Networks (KAN), based on the Kolmogorov–Arnold representation theorem, to examine a cervical cancer dataset. KAN exhibits the capacity to get equivalent or enhanced prediction accuracy while using fewer parameters and necessitating diminished processing resources. The study encompasses meticulous feature selection and preprocessing, succeeded by comprehensive model training and assessment. A comparative analysis underscores KAN’s effectiveness in providing accurate predictions, establishing it as a significant asset for improving early detection in cervical cancer screening and facilitating informed clinical decision making.