<p>This paper presents the Dhole Optimization Algorithm (DOA), an innovative method inspired from the social and hunting activities of dholes, especially their vocal communication and coordination techniques. DOA simulates distinctive behaviors, including vocalization-driven adaptive decision-making and dynamic pack formation, which improve the balance between exploration and exploitation during the optimization process. The efficacy of DOA is assessed using 23 classical benchmark functions, in addition to the CEC-2019 and CEC-2022 benchmark sets, together with a range of real-world optimization problems. The findings indicate that DOA routinely attains competitive performance relative to established metaheuristic algorithms, frequently surpassing them in convergence time and robustness, especially for complex, high-dimensional problems. This work’s primary contribution involves the development of a new nature-inspired optimization algorithm that incorporates adaptive methods inspired by dhole pack dynamics, accompanied by a comprehensive evaluation that underscores the strengths of DOA. The suggested DOA demonstrates a robust capacity to preserve variety, avoid local optima, and adjust to changing problem environments, rendering it a promising method for addressing complex optimization challenges and enhancing nature-inspired optimization methods.</p>