Wireless telecommunication is the backbone of mainstream technologies such as automation, smart vehicles, virtual reality, and unmanned aerial vehicles. Today, we are witnessing a wide-scale adoption of these technologies in our daily lives. The endless opportunities generated due to rapid deployments of new technologies have also brought about new challenges, chief among them is ensuring reliable system performance of cellular networks in mobility scenarios. Beamforming is an integral part of modern mobile networks that enable spatial selectivity and hence improved network quality. However, most of the beamforming techniques are iterative; therefore, they introduce additional unwanted latency into the system. Lately, we are witnessing an ever-increasing interest in exploiting the location of a mobile user to speed up beamforming. This paper comprehensively discusses how location-assisted beamforming strategies improve performance, such as latency and signal-to-noise ratio. Furthermore, we also show how artificial intelligence schemes such as machine learning and deep learning are also used to implement contextual beamforming techniques that exploit the user’s location information.