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The incidence of chronic kidney disease (CKD) is rising rapidly around the globe. Asymptomatic CKD is common and guideline-directed monitoring to predict CKD by various factors is underutilized. Computer-aided automated diagnostic (CAD) can play a major role to predict CKD. CAD systems such as deep learning algorithms are pivotal in disease diagnosis due to their high classification accuracy. In this paper, various clinical features of CKD were utilized and seven state-of-the-art deep learning algorithms (ANN, LSTM, GRU, Bidirectional LSTM, Bidirectional GRU, MLP, and Simple RNN) were implemented for the prediction and classification of CKD. The proposed algorithms were applied based on artificial intelligence by extracting and evaluating features using five different approaches from pre-processed and fitted CKD datasets. In this study, we have measured accuracy, precision, recall, and calculated the …