Chronic kidney failure (chronic kidney disease 'CKD') is a serious disease that related to the gradual loss of kidney function. It is considered one of the health threats in the developing and undeveloped countries At early stages, few symptoms can be detected, where the CKD may not become obvious until significant kidney function impaired occur. CKD treatment focuses on reducing the kidney damage progression by controlling the underlying cause, which requires disease detection at initial stages. In early addition, the financial burden of the treatment and future consequences of CKD requires early detection. In the present work a modified Cuckoo Search (MCS) trained Neural Network (NN) or NN-MCS based model is proposed to detect CKD. The NN-MCS model has been proposed to overcome the problem of using local search based learning algorithms to train the NNs. The NN weight vector is optimized by applying MCS for NN training. A comparative study with eminent classifiers, namely the Multilayer Perceptron Feed-forward Network (MLP-FFN) and NN based on Particle Swarm Optimization (PSO-NN). The classifiers performance is measured in terms of different performance metrics. The experimental results depicted that the NN-MCS has the ability to detect CKD more efficiently compared to any other existing model. © 2017 IEEE.