Research on Mobile Sink-Based Data Gathering Techniques in Wireless Sensor Networks (WSNs) aims to enhance energy efficiency, reduce latency, and extend network lifetime by employing one or more mobile sinks that move within the network to collect data directly from sensor nodes or cluster heads. Unlike static sinks, mobile sinks help overcome the energy-hole problem caused by uneven data forwarding loads and minimize multi-hop communication overhead. Recent studies focus on trajectory optimization, clustering, and QoS-aware sink movement strategies to balance energy consumption and ensure timely data delivery. Additionally, machine learning and metaheuristic algorithms are being applied to predict optimal sink paths and adapt to dynamic network conditions. Overall, mobile sink-based data gathering provides a scalable and energy-efficient solution for reliable data collection in large-scale and heterogeneous WSN deployments.