Ordinarily, we often use technological tools that are founded upon ML. For instance, ML in health care has greatly improved the areas of medical imaging and computer-aided diagnosis. Recent advances in ML have made these techniques flexible and resilient in their applicability to various real-world scenarios, ranging from extraordinary to mundane. Early ML techniques were rigid and incapable of tolerating any variations from the training data. Recently, ML is enjoying renewed interest. This instigates a shift in the traditional programming paradigm, where programs are written to automate tasks. The patterns learnt are used to analyze unknown data, such that it can be grouped together or mapped to the known groups. In essence, the goal of ML is to identify and exploit hidden patterns in “training” data. It goes beyond simply learning or extracting knowledge, to utilizing and improving knowledge over time and with experience. Machine learning (ML) enables a system to scrutinize data and deduce knowledge. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. There are various surveys on ML for specific areas in networking or for specific network technologies. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains.