This study introduces a novel multilevel image segmentation approach based on an enhanced Whale Optimization Algorithm (WOA). While WOA has shown promise in various optimization tasks, its performance can be limited by a tendency to get trapped in local optima. To address this challenge, we propose the K-point Strategy Improved Convergence WOA (KSICWOA), which enhances optimization efficiency by incorporating a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization strategy. The proposed KSICWOA is then applied alongside Otsu’s cross variance and Kapur’s entropy as objective functions to determine optimal thresholds for multilevel grayscale image segmentation. Experimental results on benchmark functions as well as real time images demonstrate that KSICWOA surpasses conventional optimization techniques in terms of search accuracy and convergence speed while effectively avoiding local optima. Additionally, tests conducted on standard image segmentation datasets confirm that the KSICWOA-Kapur method accurately and efficiently identifies multilevel thresholds.