According to statistics, the third most fatal disease affecting women worldwide is cervical cancer. Nearly 99.7% of occurrences of cervical cancer that result in tumors in the infected area are caused by HPV infection. To treat the condition, doctors advise early diagnosis and treatment. Due to high expense of treatment, the absence of adequate healthcare services and the symptoms' delayed onset, systematic screening for illness detection is not performed in both developed and underdeveloped nation. Machine intelligence makes early identification of a variety of illnesses, including cervical cancer. Also, it is economical and cost-effective to compute. Patients do not need to undergo time-consuming, modern medical procedures, and artificial intelligence can aid in the early identification of cervical cancer. The drawback of the current machine classification methods for illness detection is their reliance on the prediction accuracy of a single classifier. The adoption of a single classification technique does not provide the best prediction due to bias and over-fitting. This research is done using ensemble classification approach based on majority voting to deliver a proper diagnosis that addresses the patient's symptoms or concerns. As a result, the suggested paradigm grants health professionals a second opinion to aid in the early diagnosis and prompt treatment of diseases.