The International Data Corporation (IDC) estimated that total digital data created, replicated, and consumed was 4.4 Zettabytes (ZB) in the year 2013, 8 ZB in 2015, and predicted to reach 40 ZB by 2020. This massive amount of internet traffic put a great overhead on network capacity which may impact network Quality of Service (QoS) such as latency, jitter, throughput, packet loss, and load balancing. From the Internet Service Provider’s (ISP’s) perspective, understanding the possible impact of the future internet traffic on its network is critical for provisioning their network capacity in a cost-effective manner while meeting network QoS requirements. In order to achieve the above goal, one needs a framework that is capable of taking input from the traffic forecast, assign traffic load over the networks, and then identify the impact on the existing traffic QoS status (latency, jitter, packet loss, throughput, etc. In this paper, we developed a network planning framework namely Network Impact Modelling and Analysis (NIMA) that uses novel methods and techniques to predict the congestion level of the network, alerts network planners on the links that are subject to a high-risk group, indicates the impact on network-wide latency, and finally suggests an optimal routing strategy that can improve the overall network health. As part of this optimal routing task, we used Yen’s algorithm which showed performance improvement when compared with Dijkstra’s algorithm and Suurballe’s k-disjoint algorithm. For simulation purposes, we used Mininet in a combination with a floodlight controller for implementation. The experiments are performed on different sized topologies to test the effectiveness of our proposed framework. Currently we are developing a visual analytics framework to operate our developed NIMA model in a meaningful and practical way.