<p>Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs) have become a very important part of our lives. For large area networks, the static sink scenario for WSN causes the hot-spot problem. In this paper, we have proposed GATHERS (Genetic Algorithm based Transmission through Heterogeneous Energy-efficient cluster-based Routing for mobile Sink) for large-scale WSNs, resolving the hot-spot problem, and improving the CH election process in heterogeneous WSNs. In our proposed protocol, the complete networking region is partitioned into an optimal number of clusters, and a grid-based rectangular mobile sink path is built. The mobile sink moves on the trajectory and collects the information from the nearby cluster head (CH). To address the hotspot problem, the CHs and sink node are assumed to be mobile in nature. The CHs are heterogeneous, acquiring larger energy than sensor nodes, and remain the same for all rounds to perform data aggregation and data transmission. This paper utilizes Genetic Algorithm (GA) to determine the optimal locations of the CH and mobile sink on each grid. GATHERS integrates residual energy, distance between CH and sink/base station (BS), and the distance among CHs as parameters for the fitness function for CH election. In contrast, the remaining energy of CHs and distance are used as fitness parameters for the optimal location of the mobile sink on the trajectory for each cluster. The mobile sink stops at the optimal sink locations and collects information from the respective cluster head. The proposed algorithm outperforms existing algorithms, such as LEACH, SEP, EDEDA, OptiGeA, and existing GA-based sink mobility algorithms in terms of network lifetime, number of dead nodes against rounds, and the network’s remaining energy. GATHERS improves the stability period (FND) by 11.7%, the half-node death point (HND) by 3.37%, and the overall network lifetime (LND) by 6.1% compared to OptiGeA approximately.</p>