<p>Over the past few years, rising interest in UASNs underscores their importance in exploring the marine ecosystem. Every phase of implementation is crucial within its specific field. However, certain implementations have the potential to optimize ocean exploration while promoting various maritime applications. Real-time applications in UASN include oceanographic data collection, underwater surveillance, marine exploration, etc. To ensure reliable communication, it is essential to identify the optimal placement of submerged sensor nodes. This research introduces an effective localization methodology utilizing a revamped underwater grey wolf optimization method (RLCS-IUGWOM) to mitigate the stratification impact and achieve the target. To ascertain the specific geographic location of underwater sensor nodes, the sensor nodes in the 3D-UASN are initially dispersed randomly, leveraging an integration of centroid-based localization and ray theory methodology. Subsequently, the coplanarity of the underwater sensor nodes is analyzed. An improved underwater grey wolf optimization method (IUGWOM) is employed subsequently after the estimation of the location of unknown sensor nodes to acquire the precise position and compensate the stratification effect. The mathematical comparative analysis between the I-LASP and the proposed algorithm is accomplished. In 3D-UASN for both low- and high-density zones, the RLCS-IUGWOM obtains localization accuracy of 31.23% and 24.09%, respectively, and ranging accuracy of 31.61% and 40.03%, respectively. The outcomes of the mathematical simulation reveal that the proposed algorithm surpasses the existing algorithm in terms of localization and range accuracy in both low- and high-density zones in 3D-UASN.</p>