<p>The challenge of finding optimal solutions in optimizing complex engineering problems remains a significant concern across both academic research and industrial sectors. In response to this challenge, this study proposes the Trip Road Optimization (TRO) algorithm, a novel human-inspired metaheuristic designed as an effective and efficient tool for addressing complex and high-dimensional optimization problems. The algorithm emulates human decision-making behavior during a trip through three fundamental components: path scouting for global exploration, social influence for guided convergence, and adaptation for refined exploitation. Each component is modeled through dedicated mathematical formulations to ensure a balanced search strategy. The performance of TRO was comprehensively assessed using a diverse set of benchmark problems, including nineteen standard functions and two well-established test suites (CEC 2019 and CEC 2022). Furthermore, the algorithm’s practical strength is demonstrated through its successful application to real-world engineering design problems. The provided results were then compared to six well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Tunicate Swarm Algorithm (TSA), and Arctic Puffin Algorithm (APO). The obtained results demonstrate the superior performance, robustness, and computational efficiency of TRO in terms of solution quality and stability. Validation using the Wilcoxon rank-sum test and Cohen’s d effect size confirms both the statistical and practical significance of the results. The findings highlight the strong performance of the proposed algorithm and its suitability for broad optimization applications.</p>