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Cervical malignant growth is the fourth most typical reason for disease demise in women around the globe. Cervical cancer growth is related to human papillomavirus (HPV) contamination. Early screening made cervical cancer a preventable disease that results in minimizing the global burden of cervical cancer. In developing countries, women do not approach sufficient screening programs because of the costly procedures to undergo examination regularly, scarce awareness, and lack of access to the medical center. In this manner, the expectation of the individual patient's risk becomes very high. There are many risk factors relevant to malignant cervical formation. This paper proposes an approach named <i>CervDetect</i> that uses machine learning algorithms to evaluate the risk elements of malignant cervical formation. <i>CervDetect</i> uses Pearson correlation between input variables as well as with the output variable to pre-process the data. <i>CervDetect</i> uses the random forest (RF) feature selection technique to select significant features. Finally, <i>CervDetect</i> uses a hybrid approach by combining RF and shallow neural networks to detect Cervical Cancer. Results show that <i>CervDetect</i> accurately predicts cervical cancer, outperforms the state-of-the-art studies, and achieved an accuracy of 93.6%, mean squared error (MSE) error of 0.07111, false-positive rate (FPR) of 6.4%, and false-negative rate (FNR) of 100%.