Engineering problems are one of the main focuses of data science, and there is no doubt that engineering is now quickly growing in all sciences and fields. Many problems in this domain need to be examined and analyzed by massive and varied methods to help industry and organizations make more informed business decisions, especially for uncovering design, parameter values, market trends, customer preferences, and other helpful information. In addition, engineering problems demand new and sophisticated algorithms based on optimization techniques to treat problems in the real world with high accuracy and productivity. The papers published in this Special Issue (Evolutionary Process for Engineering Optimization) have covered various vital topics, enriching the state of the art in artificial intelligence, machine learning, and engineering domains. Additionally, these research papers build upon fundamental techniques and approaches previously accomplished. The creativity in the established papers resides in the methods, reviews, and experimental techniques that present an outstanding value for beneficial applications. This is one of the explanations of why this Special Issue has been named "Evolutionary Process for Engineering Optimization". There is also another explanation: practical applications need researchers, scientists, and engineers to find solutions for engineering problems consistent with current technologies and react to the near future demands. That is why researchers must utilize and develop artificial-intelligence-based optimization techniques for specific needs.