Problem Statement: Cancer is a complex genetic disease. Evidence is emerging that particular microRNAs may play a role in human cancer pathogenesis; they exhibit important regulatory roles in development, cell proliferation, cell survival and apoptosis [1]. The effectiveness in treatment and curing cancer is directly dependent on the ability to detect cancers at their earlier stages. This study developed a neural network with back-propagation learning algorithm for the prediction of microRNAs responsible from cancer pathogenesis at earlier stages. Purpose of the Study: The aim of this research was to develop a nonlinear empirical model to predict a score value for specific microRNAs responsible from cancer pathogenesis by using micro-array data. Methods: The Back-Propagation Multi Layer perceptron (BPMLP) for the prediction of score value. The supervised learning algoritm is used during the training and testing stages. The number of inputs is fixed at two and the number of output is one. The 677 micro-array data were collected from a cancer microarray database and data-mining platform called Oncomine. Testing data are classified into randomly selected four groups of data. Findings and Results: In this work, a BPMLP, is used to establish the relationship between the microRNAs, p-value and score value. Accuracy rate was adjusted to 80 % and 90 %. The best performance was obtained with 20 neurons in the hidden layer and learning parameter as 0.00099. Conclusions and Recommendations: BPMLP is obtained by developing forward propagation phase and sensitivity network. It is shown that, the generalization capability can be significantly improved by optimizing some of the parameters such as learning parameter, and neuron number. Since microRNAs may play a role in human cancer pathogenesis, we have shown that, it is possible to predict relationships of microRNAs, p-values with different score values. Therefore, by using BPMLP, it is possible to predict microRNAs responsible from cancer pathogenesis at earlier stages.