Computer network complexity has increased in the last decades due to the introduction of various concepts, leaving network maintainers in hardship to manage such huge and tangled networks. In this study, we aim to aid service providers to optimize and automate their networks. Currently, network maintainers perform a vast number of explicit measurements, which has a negative effect on the network’s health and stability. Depending on the service’s nature, measurements are either made at service initiation as in the case of server-client selection or continuously done to monitor the quality of service as in the case of quality assurance applications. We intend to apply artificial intelligence to minimize the dependency on such explicit measurements and hence, optimize the network with minimal cost. From the two types of applications, we focus on distributed delay measurements for Esports server-client selection problem as well as network automation and failure mitigation task done by Internet service providers. In large-scale networks, it is impractical to measure the delay between every node explicitly. As a result, we propose an AI-based delay measurement estimator system. The system’s inputs are just the source and destination nodes’ IP-addresses. Network maintainers continuously monitor their network status to detect any sudden change in the network and take suitable action(s) to keep the network in the best conditions. We propose an ML-based action recommender engine that is able to identify the current network status and suggest a set of actions that restore the network to its optimum state.