The increasing demand for sustainable energy solutions necessitates advanced optimization techniques in wind energy systems, where meta-heuristic algorithms like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) have shown considerable promise. This paper proposes the novel integration of Ant Colony Optimization (ACO) within an inquiry-based learning framework to improve critical thinking and problem-solving abilities in physics education. Targeting undergraduate physics students at Nakhon Phanom University, Thailand, the research focuses on applying ACO to optimize wind turbine configurations, thereby simulating complex, real-world challenges in wind energy management. The effectiveness of this pedagogical approach was assessed through pre- and post-tests, evaluating students' critical thinking, problem-solving skills, and scientific attitudes. The findings reveal significant improvements in both academic performance and student engagement, underscoring the value of incorporating ACO into STEM education. This study offers important implications for enhancing physics curricula through the integration of advanced optimization techniques, equipping students with the skills necessary for future roles in the renewable energy sector.