Software Defined Networking (SDN) is gaining momentum not only in research but also in IT industry representing the drivers of 5G networks, due to its capabilities of increasing the flexibility of a network and address a variety of network challenges, by logically centralizing the intelligence in software-based controllers. Thanks to Machine Learning (ML) techniques, the network performances and utilization can be optimized and enhanced. Neural Networks (NN) and Reinforcement Learning (RL), in particular, have demonstrated great success in cooperating with complex problems arising in network operation and management. To this end, we exploit in this paper, an SDN-based rules placement approach that aims to dynamically predict the traffic congestion by using mainly NN and learn optimal paths and reroute traffic to improve network utilization by deploying a Deep Q-Network (DQN) agent. To this end, we first formulate the Quality-of-Service (QoS)-aware routing problem as a Linear Program (LP), whose objective is to minimize the end-to-end (E2E) delay and link utilization. Then, we propose a simple yet efficient heuristic algorithm to solve it. Numerical results through emulation using ONOS controller and Mininet demonstrate that the proposed approach can significantly improve network performances in terms of decreasing the link utilization, the packet loss and the E2E delay.