5G cellular networks are characterized by a service-based architecture (SBA) where physical and virtual network functions (NFs) interact with each other. In conjunction with multi-access edge computing (MEC), 5G systems are expected to enable a wide range of advanced applications for vertical industries as well as over-the-top (OTT) service providers. Although MEC typically processes user-plane data, in this article, we exploit it to process control-plane data via the 5G network exposure function (NEF), enabling new context-aware applications. Based on cell-specific radio access network (RAN) signaling, we envision a machine learning (ML) solution that learns the user-context evolution, where the ML engine runs on a MEC host and its prediction is used to change the network setup for a given application. As an example, to address the challenging, fast-changing vehicular channel, we describe a predictive fallback mechanism for Voice Over Internet Protocol (VoIP) calls, wherein critical channel conditions are predicted to anticipate the fallback to, e.g., a traditional voice call, thus ensuring service continuity to the end user.