In this work, a new multilevel image segmentation method based on an improved Whale Optimization Algorithm (WOA) is presented. Although WOA has demonstrated potential in a number of optimization problems, its efficacy may be hampered by its vulnerability to local optima. In order to overcome this, we suggest a Mixed-Strategy Improved Convergence WOA (MSICWOA) that strengthens its optimization capabilities by combining a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization approach. After that, the MSICWOA is used with Otsu cross variance and Kapur entropy as objective functions to identify the best thresholds for multilevel grayscale image segmentation. Results from experiments on benchmark functions show that MSICWOA outperforms other optimization methods in terms of search accuracy and convergence speed. Moreover, it successfully overcomes local optima. Experiments on image segmentation using typical datasets verify that the MSICWOA-Kapur technique is effective in precisely and quickly identifying multilevel thresholds.