The integration of Explainable Artificial Intelligence (XAI) and Evolutionary Computation (EC) has opened up new possibilities for solving complex, real-world problems across various industries. This chapter explores the synergy between these two fields, showcasing how the combination of XAI’s interpretability and EC’s optimization capabilities can enhance decision-making and trust in AI systems. We focus on key applications in aviation, automotive, and healthcare, demonstrating how XAI and EC are used to improve predictive maintenance, crashworthiness optimization, and drug discovery, among other tasks. Additionally, we highlight emerging applications in fields such as nanotechnology and time-series classification. By providing interpretable and optimized solutions, XAI and EC contribute to the development of AI systems that are not only effective but also transparent and trustworthy. The chapter concludes by discussing future directions for research, including the need for more sophisticated XAI methods tailored to evolutionary algorithms, real-time data integration, and advanced counterfactual explanations. These advancements are poised to further enhance the impact of XAI and EC in both research and industry.