From 1G to 5G and beyond cellular network technologies evolve in terms of technical advancement, efficiency, and traffic. Increase in accessibility and data utilization are the notable factors that are causing high network traffic. Higher the connectivity and mobility of cellular devices, higher will be the network traffic. As the transmission rate increases, the possibility of fault occurrence also increases. Continuous monitoring of network parameters and finding the fault on time are the key factors in determining consistency of network. Cellular network which is highly dynamic than usual networks needs intelligent way of fault handling. The monitoring and fault identification human overhead are unpredictable and very high and can cause errors as well. The research in modelling intelligent network fault identification system can simplify human efforts and improve efficiency. The research is on real time data of 4G cellular network including various network parameters like uplink threshold and identify the behaviour of data usual or unusual to predict the fault occurrence. The numerical dataset on various network parameters on usual and unusual behaviours helps to simplify the feature extraction part and to reduce the complexity of the model. The model is finalized based on various machine learning-deep learning model analysis.