In numerous sectors of civil engineering, machine learning has shown to be an efficient technique for resolving complex engineering problems. The availability of data, computing power, and the complexity of programming techniques have all expanded rapidly. As a consequence of these factors, the use of machine learning technologies in the fields of civil engineering has developed. The machine learning techniques give a mechanism that is both efficient and rapid for decomposing complex occurrences into more manageable mathematical phases. This book will serve as a platform for cutting-edge applications of machine learning in civil engineering, such as the construction of prediction models, optimization problems, data analysis, and system behavior testing scenarios. This book addresses the vast potential advantages of machine learning in a number of civil engineering domains and challenges by analyzing data from several studies. The research focuses on the application of machine learning to a variety of civil engineering disciplines, including structural engineering, environmental engineering, engineering project management, construction management, hydrology, hydraulic engineering, geotechnical engineering, coastal and ocean engineering, and transportation and traffic engineering. The collection also seeks review articles on civil engineering applications that use artificial intelligence technology (such ANN, fuzzy systems, expert systems, and swarm intelligence). This special issue aims to offer a forum for discussion of these topics, serve as a repository for important accomplishments in AI-related civil engineering, and push the limits of AI-enabled knowledge discovery and technological innovation. The book covers both fundamental research and technical applications of cutting-edge AI technology. Included among these technologies are big data, blockchain, cloud computing, the Internet of Things, computer vision, natural language processing, augmented displays, machine learning, and deep learning. Utilizing these technologies may be advantageous for many parts of civil engineering. the prediction of the behavior of structural systems and its components; monitoring and assessing the dependability and structural integrity; predicting the mechanical properties of materials used in civil engineering and construction; using stochastic models and machine learning to simulate and anticipate hydrological and climatological occurrences; Support for intelligent transportation system decision-making; Modeling applications for investigating, controlling, and optimizing smart city traffic; regulating and monitoring traffic and administering public transportations.