On a macro scale, the issue of the spread of infectious disease threatens society having a significant impact on both human lives and the economy. The pattern of contagious diseases differs from region to region because of different reasons where the disease incidence data collected by the country is considered for containing the information. Even though the application of big data has widened its wings into the fields of marketing and earth sciences, still the area of public health remains dependent on conventional surveillance systems, waiting to utilize the fruits of a big data revolution. The need for a new generation big data surveillance system has risen to achieve the flexible, regular, and rapid tracking of infectious diseases, particularly during emerging pathogens. The prime advantage of RNNs is the availability of contextual information during the mapping of IO sequences. This examination work brings out an enhanced RNN model for anticipating infectious illnesses. Enhanced RNN produces Accuracy 94.08%, Precision 0.92 and Recall 0.82. The tool used for execution is Python.