Caching contents at the edge of network is considered to be a cost-effective solution to cope with ongoing traffic growth and address the backhaul bottleneck problem in wireless networks. However, the inherent characteristics of wireless networks, including the high mobility of users and restricted storage capability of edge nodes, cause many difficulties in the design of caching schemes. Driven by the recent advancements in Machine Learning (ML), learning-based proactive caching schemes are able to accurately predict content popularity and improve cache efficiency, but they need gather and analyse users’ content retrieval history and personal data, leading to privacy concerns. To address these challenges, this research mainly focuses on the design of learning-based caching schemes to improve caching efficiency and protect user privacy in various modern networks, such as Fifth Generation Mobile Networks (5G), Internet-of-Vehicles (IoV), and Fog Radio Access Networks (FRANs).