While extensive research has been carried out on the management of various types of infrastructure assets, limited research has been carried out for coastal structures. The rapid growth of the world population living in low-lying areas within close range to the shoreline over the past century, compounded by the impact of global climate change on shoreline hydrodynamics; have increased the importance of coastal infrastructure management. Climate change has recently increased storm intensities in addition to decreasing storm return periods; imposing greater risks to life and property. The aim of this research is to provide an artificial-intelligence-based modeling methodology for deterioration prediction, and optimization of repair, maintenance, and rehabilitation costs of various sorts of coastal protection structures over their life time. The coastal protection structures in Alexandria were taken as the case study. The city of Alexandria is located on the northern coast of Egypt, overlooking the Mediterranean Sea; with a waterfront extending over 43 km. Alexandria is Egypt’s main port and industrial center. Its beaches attract millions of summer residents yearly, and also its land and real estate prices are among the highest in Egypt, with the most expensive lying within very close range from the shoreline. The city has been exposed to severe winter storms between 2003 and 2010, and suffers from a compounded increase in relative Main Sea Level (MSL) caused by climate change and seismic subsidence. An Asset Inventory Database (AID) for Alexandria’s coastal assets was developed. Established visual inspection and condition rating procedures were followed to obtain a current Structural Condition Index (SI) and a Structural Condition Matrix (SCM) for each structure, considering a single inspection point. SI’s and SCM’s are classified into severity ranges. Functional Condition Indices (FI’s) were also calculated to be used later on to rank the priority structures as to maintenance and repair. Transition probabilities between each of the deterioration severity ranges were calculated using backward analysis. Such probabilities were then utilized to formulate the structure’s Markov Chain (MC) transition probability matrix, enabling the prediction of future deterioration. By calculating the average maintenance and repair per meter run of every coastal structure, corresponding to the condition of the structure, a Genetic-Algorithm (GA) – based Life-Cycle Cost (LCC) optimization modeling was then constructed with the aim to minimize the total LCC for the entire coastal assets over the next 50 years, while achieving the minimum reliability of structures.