Throughout the development of energy efficient routing protocol for wireless sensor network clustering technique has been widely adopted approach. The Selection of cluster heads is also very important for the energy efficiency of the network. In the past, researchers have proposed multiple routing protocols; however, problems are still alive and need to be resolved because of the diversity of WSN applications. This article presents an energy-efficient routing protocol using the advanced approaches of Artificial Intelligence, the most promising field of computer science currently providing the best solutions. The proposed model uses the Deep Q-network to select the cluster head. Moreover, collected data at the cluster head is generalized as low, moderate, and high values using the fuzzy logic technique. After that, the Predictive coding theory algorithm is used for the data compression, and the lossy compression technique is applied to the data. Its compressed form also gives complete information of the data in small size and is delivered to the base station. Again, the transmitted data is reconstructed into its actual format. In the end, to justify the performance of the newly designed routing protocol, simulations are performed using the Matlab tool, and its results are evaluated in quality of service matrices and compared with well-known routing protocols (pp.18-24).