Civil engineering structures including masonry and concrete structures, seen as buildings, bridges, and even in other applications like monuments are prone to deterioration revealed as cracks, spalling, efflorescence, etc. The amount of money invested in building, time taken to build, quality of the original works, and most importantly the safety and life of the people inhabiting and using the structures, warrant such structures to be monitored and constantly assessed to confirm their utility safety to life. In this direction, a new technique called Damage detection enables us identification and recognition of defects in the structure, quantify them, and even propose measures by which these defects may be managed including predictions of the effects on the remaining structural life. Artificial intelligence (AI) uses data to machine and ensure human-like efficient results have been achieved. In the present study, the detailed study uncovers the present applications and uses of AI, and ML in the identification and recognition of damage present in masonry and concrete structures in the field of structural health monitoring and the utility of AI in damage detection from a different point of view. The findings from the study could help civil engineers about the different aspects of how AI and ML might help in the detection and monitoring of problems by monitoring structural health using simulations of real-life situations. It will be like a detailed theoretical solution to a practical problem. After thorough study, it can be deduced that AI is a powerful tool applied to the detection of damage in concrete structures, extending also to historical civil works and monuments, yielding quick solutions and reliable results with heightened accuracy.